--- _id: '13053' abstract: - lang: eng text: 'Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as pruning. Thus, dense models trained via CrAM should be compressible post-training, in a single step, without significant accuracy loss. Experimental results on standard benchmarks, such as residual networks for ImageNet classification and BERT models for language modelling, show that CrAM produces dense models that can be more accurate than the standard SGD/Adam-based baselines, but which are stable under weight pruning: specifically, we can prune models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90% with reasonable (∼1%) accuracy loss, which is competitive with gradual compression methods. Additionally, CrAM can produce sparse models which perform well for transfer learning, and it also works for semi-structured 2:4 pruning patterns supported by GPU hardware. The code for reproducing the results is available at this https URL .' acknowledged_ssus: - _id: ScienComp acknowledgement: "AP, EK, DA received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). AV acknowledges the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-21-CE48-0016 (project COMCOPT). We further acknowledge the support from the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp)-" article_processing_charge: No author: - first_name: Elena-Alexandra full_name: Peste, Elena-Alexandra id: 32D78294-F248-11E8-B48F-1D18A9856A87 last_name: Peste - first_name: Adrian full_name: Vladu, Adrian last_name: Vladu - first_name: Eldar full_name: Kurtic, Eldar id: 47beb3a5-07b5-11eb-9b87-b108ec578218 last_name: Kurtic - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. In: 11th International Conference on Learning Representations .' apa: 'Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .' chicago: 'Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations , n.d.' ieee: 'E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda .' ista: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. 11th International Conference on Learning Representations . ICLR: International Conference on Learning Representations.' mla: 'Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th International Conference on Learning Representations .' short: E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , n.d. conference: end_date: 2023-05-05 location: 'Kigali, Rwanda ' name: 'ICLR: International Conference on Learning Representations' start_date: 2023-05-01 date_created: 2023-05-23T11:36:18Z date_published: 2023-05-01T00:00:00Z date_updated: 2023-06-01T12:54:45Z department: - _id: GradSch - _id: DaAl - _id: ChLa ec_funded: 1 external_id: arxiv: - '2207.14200' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/pdf?id=_eTZBs-yedr month: '05' oa: 1 oa_version: Preprint project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: '11th International Conference on Learning Representations ' publication_status: accepted quality_controlled: '1' related_material: record: - id: '13074' relation: dissertation_contains status: public status: public title: 'CrAM: A Compression-Aware Minimizer' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14410' abstract: - lang: eng text: This paper focuses on the implementation details of the baseline methods and a recent lightweight conditional model extrapolation algorithm LIMES [5] for streaming data under class-prior shift. LIMES achieves superior performance over the baseline methods, especially concerning the minimum-across-day accuracy, which is important for the users of the system. In this work, the key measures to facilitate reproducibility and enhance the credibility of the results are described. alternative_title: - LNCS article_processing_charge: No author: - first_name: Paulina full_name: Tomaszewska, Paulina last_name: Tomaszewska - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Tomaszewska P, Lampert C. On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift. In: International Workshop on Reproducible Research in Pattern Recognition. Vol 14068. Springer Nature; 2023:67-73. doi:10.1007/978-3-031-40773-4_6' apa: 'Tomaszewska, P., & Lampert, C. (2023). On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift. In International Workshop on Reproducible Research in Pattern Recognition (Vol. 14068, pp. 67–73). Montreal, Canada: Springer Nature. https://doi.org/10.1007/978-3-031-40773-4_6' chicago: Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.” In International Workshop on Reproducible Research in Pattern Recognition, 14068:67–73. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-40773-4_6. ieee: P. Tomaszewska and C. Lampert, “On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift,” in International Workshop on Reproducible Research in Pattern Recognition, Montreal, Canada, 2023, vol. 14068, pp. 67–73. ista: 'Tomaszewska P, Lampert C. 2023. On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift. International Workshop on Reproducible Research in Pattern Recognition. RRPR: Reproducible Research in Pattern Recognition, LNCS, vol. 14068, 67–73.' mla: Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.” International Workshop on Reproducible Research in Pattern Recognition, vol. 14068, Springer Nature, 2023, pp. 67–73, doi:10.1007/978-3-031-40773-4_6. short: P. Tomaszewska, C. Lampert, in:, International Workshop on Reproducible Research in Pattern Recognition, Springer Nature, 2023, pp. 67–73. conference: end_date: 2022-08-21 location: Montreal, Canada name: 'RRPR: Reproducible Research in Pattern Recognition' start_date: 2022-08-21 date_created: 2023-10-08T22:01:18Z date_published: 2023-08-20T00:00:00Z date_updated: 2023-10-09T06:48:02Z day: '20' department: - _id: ChLa doi: 10.1007/978-3-031-40773-4_6 intvolume: ' 14068' language: - iso: eng month: '08' oa_version: None page: 67-73 publication: International Workshop on Reproducible Research in Pattern Recognition publication_identifier: eissn: - 1611-3349 isbn: - '9783031407727' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 14068 year: '2023' ... --- _id: '14921' abstract: - lang: eng text: Neural collapse (NC) refers to the surprising structure of the last layer of deep neural networks in the terminal phase of gradient descent training. Recently, an increasing amount of experimental evidence has pointed to the propagation of NC to earlier layers of neural networks. However, while the NC in the last layer is well studied theoretically, much less is known about its multi-layered counterpart - deep neural collapse (DNC). In particular, existing work focuses either on linear layers or only on the last two layers at the price of an extra assumption. Our paper fills this gap by generalizing the established analytical framework for NC - the unconstrained features model - to multiple non-linear layers. Our key technical contribution is to show that, in a deep unconstrained features model, the unique global optimum for binary classification exhibits all the properties typical of DNC. This explains the existing experimental evidence of DNC. We also empirically show that (i) by optimizing deep unconstrained features models via gradient descent, the resulting solution agrees well with our theory, and (ii) trained networks recover the unconstrained features suitable for the occurrence of DNC, thus supporting the validity of this modeling principle. acknowledgement: M. M. is partially supported by the 2019 Lopez-Loreta Prize. The authors would like to thank Eugenia Iofinova, Bernd Prach and Simone Bombari for valuable feedback on the manuscript. alternative_title: - NeurIPS article_processing_charge: No author: - first_name: Peter full_name: Súkeník, Peter id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c last_name: Súkeník - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. In: 37th Annual Conference on Neural Information Processing Systems.' apa: Súkeník, P., Mondelli, M., & Lampert, C. (n.d.). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States. chicago: Súkeník, Peter, Marco Mondelli, and Christoph Lampert. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” In 37th Annual Conference on Neural Information Processing Systems, n.d. ieee: P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably optimal for the deep unconstrained features model,” in 37th Annual Conference on Neural Information Processing Systems, New Orleans, LA, United States. ista: 'Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. 37th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, NeurIPS, .' mla: Súkeník, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” 37th Annual Conference on Neural Information Processing Systems. short: P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, n.d. conference: end_date: 2023-12-16 location: New Orleans, LA, United States name: 'NeurIPS: Neural Information Processing Systems' start_date: 2023-12-10 date_created: 2024-02-02T11:17:41Z date_published: 2023-12-15T00:00:00Z date_updated: 2024-02-06T07:53:26Z day: '15' department: - _id: MaMo - _id: ChLa external_id: arxiv: - '2305.13165' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2305.13165' month: '12' oa: 1 oa_version: Preprint project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 37th Annual Conference on Neural Information Processing Systems publication_status: inpress quality_controlled: '1' status: public title: Deep neural collapse is provably optimal for the deep unconstrained features model type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '15039' abstract: - lang: eng text: 'A crucial property for achieving secure, trustworthy and interpretable deep learning systems is their robustness: small changes to a system''s inputs should not result in large changes to its outputs. Mathematically, this means one strives for networks with a small Lipschitz constant. Several recent works have focused on how to construct such Lipschitz networks, typically by imposing constraints on the weight matrices. In this work, we study an orthogonal aspect, namely the role of the activation function. We show that commonly used activation functions, such as MaxMin, as well as all piece-wise linear ones with two segments unnecessarily restrict the class of representable functions, even in the simplest one-dimensional setting. We furthermore introduce the new N-activation function that is provably more expressive than currently popular activation functions. We provide code at this https URL.' article_number: '2311.06103' article_processing_charge: No author: - first_name: Bernd full_name: Prach, Bernd id: 2D561D42-C427-11E9-89B4-9C1AE6697425 last_name: Prach - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv. doi:10.48550/ARXIV.2311.06103 apa: Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103 chicago: Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More Expressive with N-Activations.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2311.06103. ieee: B. Prach and C. Lampert, “1-Lipschitz neural networks are more expressive with N-activations,” arXiv. . ista: Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv, 2311.06103. mla: Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More Expressive with N-Activations.” ArXiv, 2311.06103, doi:10.48550/ARXIV.2311.06103. short: B. Prach, C. Lampert, ArXiv (n.d.). date_created: 2024-02-28T17:59:32Z date_published: 2023-11-10T00:00:00Z date_updated: 2024-03-04T07:02:39Z day: '10' department: - _id: GradSch - _id: ChLa doi: 10.48550/ARXIV.2311.06103 external_id: arxiv: - '2311.06103' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2311.06103 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: 1-Lipschitz neural networks are more expressive with N-activations type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '12660' abstract: - lang: eng text: 'We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning. XCLP estimates a data graph jointly from the data of multiple clients and computes labels for the unlabeled data by propagating label information across the graph. To avoid clients having to share their data with anyone, XCLP employs two cryptographically secure protocols: secure Hamming distance computation and secure summation. We demonstrate two distinct applications of XCLP within federated learning. In the first, we use it in a one-shot way to predict labels for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled training data in a federated semi-supervised setting. Experiments on both real federated and standard benchmark datasets show that in both applications XCLP achieves higher classification accuracy than alternative approaches.' article_number: '2210.06434' article_processing_charge: No author: - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott - first_name: Michelle X full_name: Yeo, Michelle X id: 2D82B818-F248-11E8-B48F-1D18A9856A87 last_name: Yeo - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. arXiv. doi:10.48550/arXiv.2210.06434 apa: Scott, J. A., Yeo, M. X., & Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. arXiv. https://doi.org/10.48550/arXiv.2210.06434 chicago: Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client Label Propagation for Transductive Federated Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2210.06434. ieee: J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for transductive federated learning,” arXiv. . ista: Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. arXiv, 2210.06434. mla: Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive Federated Learning.” ArXiv, 2210.06434, doi:10.48550/arXiv.2210.06434. short: J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.). date_created: 2023-02-20T08:21:50Z date_published: 2022-10-12T00:00:00Z date_updated: 2023-02-21T08:20:18Z day: '12' ddc: - '004' department: - _id: ChLa doi: 10.48550/arXiv.2210.06434 external_id: arxiv: - '2210.06434' file: - access_level: open_access checksum: 7ab20543fd4393f14fb857ce2e4f03c6 content_type: application/pdf creator: chl date_created: 2023-02-20T08:21:35Z date_updated: 2023-02-20T08:21:35Z file_id: '12661' file_name: 2210.06434.pdf file_size: 291893 relation: main_file success: 1 file_date_updated: 2023-02-20T08:21:35Z has_accepted_license: '1' language: - iso: eng month: '10' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Cross-client Label Propagation for transductive federated learning tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '12662' abstract: - lang: eng text: 'Modern machine learning tasks often require considering not just one but multiple objectives. For example, besides the prediction quality, this could be the efficiency, robustness or fairness of the learned models, or any of their combinations. Multi-objective learning offers a natural framework for handling such problems without having to commit to early trade-offs. Surprisingly, statistical learning theory so far offers almost no insight into the generalization properties of multi-objective learning. In this work, we make first steps to fill this gap: we establish foundational generalization bounds for the multi-objective setting as well as generalization and excess bounds for learning with scalarizations. We also provide the first theoretical analysis of the relation between the Pareto-optimal sets of the true objectives and the Pareto-optimal sets of their empirical approximations from training data. In particular, we show a surprising asymmetry: all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa.' article_number: '2208.13499' article_processing_charge: No author: - first_name: Peter full_name: Súkeník, Peter id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c last_name: Súkeník - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv. doi:10.48550/arXiv.2208.13499 apa: Súkeník, P., & Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv. https://doi.org/10.48550/arXiv.2208.13499 chicago: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2208.13499. ieee: P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” arXiv. . ista: Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv, 2208.13499. mla: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” ArXiv, 2208.13499, doi:10.48550/arXiv.2208.13499. short: P. Súkeník, C. Lampert, ArXiv (n.d.). date_created: 2023-02-20T08:23:06Z date_published: 2022-08-29T00:00:00Z date_updated: 2023-02-21T08:24:55Z day: '29' ddc: - '004' department: - _id: ChLa doi: 10.48550/arXiv.2208.13499 external_id: arxiv: - '2208.13499' has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2208.13499' month: '08' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Generalization in Multi-objective machine learning type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '12495' abstract: - lang: eng text: "Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of\r\nmachine learning with far-reaching societal impact. However, existing fair learning methods\r\nare vulnerable to accidental or malicious artifacts in the training data, which can cause\r\nthem to unknowingly produce unfair classifiers. In this work we address the problem of\r\nfair learning from unreliable training data in the robust multisource setting, where the\r\navailable training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm\r\nthat identifies and suppresses those data sources that would have a negative impact on\r\nfairness or accuracy if they were used for training. As such, FLEA is not a replacement of\r\nprior fairness-aware learning methods but rather an augmentation that makes any of them\r\nrobust against unreliable training data. We show the effectiveness of our approach by a\r\ndiverse range of experiments on multiple datasets. Additionally, we prove formally that\r\n–given enough data– FLEA protects the learner against corruptions as long as the fraction of\r\naffected data sources is less than half. Our source code and documentation are available at\r\nhttps://github.com/ISTAustria-CVML/FLEA." acknowledged_ssus: - _id: ScienComp acknowledgement: 'The authors would like to thank Bernd Prach, Elias Frantar, Alexandra Peste, Mahdi Nikdan, and Peter Súkeník for their helpful feedback. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). This publication was made possible by an ETH AI Center postdoctoral fellowship granted to Nikola Konstantinov. Eugenia Iofinova was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. ' article_processing_charge: No article_type: original author: - first_name: Eugenia B full_name: Iofinova, Eugenia B id: f9a17499-f6e0-11ea-865d-fdf9a3f77117 last_name: Iofinova orcid: 0000-0002-7778-3221 - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. 2022.' apa: 'Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.' chicago: 'Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” Transactions on Machine Learning Research. ML Research Press, 2022.' ieee: 'E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust fair multisource learning from unreliable training data,” Transactions on Machine Learning Research. ML Research Press, 2022.' ista: 'Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research.' mla: 'Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” Transactions on Machine Learning Research, ML Research Press, 2022.' short: E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022). date_created: 2023-02-02T20:29:57Z date_published: 2022-12-22T00:00:00Z date_updated: 2023-02-23T10:30:54Z day: '22' ddc: - '000' department: - _id: ChLa external_id: arxiv: - '2106.11732' file: - access_level: open_access checksum: 97c8a8470759cab597abb973ca137a3b content_type: application/pdf creator: dernst date_created: 2023-02-23T10:30:04Z date_updated: 2023-02-23T10:30:04Z file_id: '12673' file_name: 2022_TMLR_Iofinova.pdf file_size: 1948063 relation: main_file success: 1 file_date_updated: 2023-02-23T10:30:04Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/forum?id=XsPopigZXV month: '12' oa: 1 oa_version: Published Version project: - _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A grant_number: ' W1260-N35' name: Vienna Graduate School on Computational Optimization publication: Transactions on Machine Learning Research publication_identifier: issn: - 2835-8856 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: link: - description: source code relation: software url: https://github.com/ISTAustria-CVML/FLEA status: public title: 'FLEA: Provably robust fair multisource learning from unreliable training data' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '11839' abstract: - lang: eng text: "It is a highly desirable property for deep networks to be robust against\r\nsmall input changes. One popular way to achieve this property is by designing\r\nnetworks with a small Lipschitz constant. In this work, we propose a new\r\ntechnique for constructing such Lipschitz networks that has a number of\r\ndesirable properties: it can be applied to any linear network layer\r\n(fully-connected or convolutional), it provides formal guarantees on the\r\nLipschitz constant, it is easy to implement and efficient to run, and it can be\r\ncombined with any training objective and optimization method. In fact, our\r\ntechnique is the first one in the literature that achieves all of these\r\nproperties simultaneously. Our main contribution is a rescaling-based weight\r\nmatrix parametrization that guarantees each network layer to have a Lipschitz\r\nconstant of at most 1 and results in the learned weight matrices to be close to\r\northogonal. Hence we call such layers almost-orthogonal Lipschitz (AOL).\r\nExperiments and ablation studies in the context of image classification with\r\ncertified robust accuracy confirm that AOL layers achieve results that are on\r\npar with most existing methods. Yet, they are simpler to implement and more\r\nbroadly applicable, because they do not require computationally expensive\r\nmatrix orthogonalization or inversion steps as part of the network\r\narchitecture. We provide code at https://github.com/berndprach/AOL." alternative_title: - LNCS article_processing_charge: No author: - first_name: Bernd full_name: Prach, Bernd id: 2D561D42-C427-11E9-89B4-9C1AE6697425 last_name: Prach - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Prach B, Lampert C. Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In: Computer Vision – ECCV 2022. Vol 13681. Springer Nature; 2022:350-365. doi:10.1007/978-3-031-19803-8_21' apa: 'Prach, B., & Lampert, C. (2022). Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol. 13681, pp. 350–365). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19803-8_21' chicago: Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks.” In Computer Vision – ECCV 2022, 13681:350–65. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-19803-8_21. ieee: B. Prach and C. Lampert, “Almost-orthogonal layers for efficient general-purpose Lipschitz networks,” in Computer Vision – ECCV 2022, Tel Aviv, Israel, 2022, vol. 13681, pp. 350–365. ista: 'Prach B, Lampert C. 2022. Almost-orthogonal layers for efficient general-purpose Lipschitz networks. Computer Vision – ECCV 2022. ECCV: European Conference on Computer Vision, LNCS, vol. 13681, 350–365.' mla: Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks.” Computer Vision – ECCV 2022, vol. 13681, Springer Nature, 2022, pp. 350–65, doi:10.1007/978-3-031-19803-8_21. short: B. Prach, C. Lampert, in:, Computer Vision – ECCV 2022, Springer Nature, 2022, pp. 350–365. conference: end_date: 2022-10-27 location: Tel Aviv, Israel name: 'ECCV: European Conference on Computer Vision' start_date: 2022-10-23 date_created: 2022-08-12T15:09:47Z date_published: 2022-10-23T00:00:00Z date_updated: 2023-05-03T08:00:46Z day: '23' department: - _id: GradSch - _id: ChLa doi: 10.1007/978-3-031-19803-8_21 external_id: arxiv: - '2208.03160' intvolume: ' 13681' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2208.03160' month: '10' oa: 1 oa_version: Preprint page: 350-365 publication: Computer Vision – ECCV 2022 publication_identifier: eisbn: - '9783031198038' isbn: - '9783031198021' publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Almost-orthogonal layers for efficient general-purpose Lipschitz networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 13681 year: '2022' ... --- _id: '10752' abstract: - lang: eng text: 'The digitalization of almost all aspects of our everyday lives has led to unprecedented amounts of data being freely available on the Internet. In particular social media platforms provide rich sources of user-generated data, though typically in unstructured form, and with high diversity, such as written in many different languages. Automatically identifying meaningful information in such big data resources and extracting it efficiently is one of the ongoing challenges of our time. A common step for this is sentiment analysis, which forms the foundation for tasks such as opinion mining or trend prediction. Unfortunately, publicly available tools for this task are almost exclusively available for English-language texts. Consequently, a large fraction of the Internet users, who do not communicate in English, are ignored in automatized studies, a phenomenon called rare-language discrimination.In this work we propose a technique to overcome this problem by a truly multi-lingual model, which can be trained automatically without linguistic knowledge or even the ability to read the many target languages. The main step is to combine self-annotation, specifically the use of emoticons as a proxy for labels, with multi-lingual sentence representations.To evaluate our method we curated several large datasets from data obtained via the free Twitter streaming API. The results show that our proposed multi-lingual training is able to achieve sentiment predictions at the same quality level for rare languages as for frequent ones, and in particular clearly better than what mono-lingual training achieves on the same data. ' article_processing_charge: No author: - first_name: Jasmin full_name: Lampert, Jasmin last_name: Lampert - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0002-4561-241X citation: ama: 'Lampert J, Lampert C. Overcoming rare-language discrimination in multi-lingual sentiment analysis. In: 2021 IEEE International Conference on Big Data. IEEE; 2022:5185-5192. doi:10.1109/bigdata52589.2021.9672003' apa: 'Lampert, J., & Lampert, C. (2022). Overcoming rare-language discrimination in multi-lingual sentiment analysis. In 2021 IEEE International Conference on Big Data (pp. 5185–5192). Orlando, FL, United States: IEEE. https://doi.org/10.1109/bigdata52589.2021.9672003' chicago: Lampert, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis.” In 2021 IEEE International Conference on Big Data, 5185–92. IEEE, 2022. https://doi.org/10.1109/bigdata52589.2021.9672003. ieee: J. Lampert and C. Lampert, “Overcoming rare-language discrimination in multi-lingual sentiment analysis,” in 2021 IEEE International Conference on Big Data, Orlando, FL, United States, 2022, pp. 5185–5192. ista: 'Lampert J, Lampert C. 2022. Overcoming rare-language discrimination in multi-lingual sentiment analysis. 2021 IEEE International Conference on Big Data. Big Data: International Conference on Big Data, 5185–5192.' mla: Lampert, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis.” 2021 IEEE International Conference on Big Data, IEEE, 2022, pp. 5185–92, doi:10.1109/bigdata52589.2021.9672003. short: J. Lampert, C. Lampert, in:, 2021 IEEE International Conference on Big Data, IEEE, 2022, pp. 5185–5192. conference: end_date: 2021-12-18 location: Orlando, FL, United States name: 'Big Data: International Conference on Big Data' start_date: 2021-12-15 date_created: 2022-02-10T14:08:23Z date_published: 2022-01-13T00:00:00Z date_updated: 2023-08-02T14:27:50Z day: '13' department: - _id: ChLa doi: 10.1109/bigdata52589.2021.9672003 external_id: isi: - '000800559505036' isi: 1 language: - iso: eng month: '01' oa_version: None page: 5185-5192 publication: 2021 IEEE International Conference on Big Data publication_identifier: isbn: - '9781665439022' publication_status: published publisher: IEEE quality_controlled: '1' status: public title: Overcoming rare-language discrimination in multi-lingual sentiment analysis type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 year: '2022' ... --- _id: '12161' abstract: - lang: eng text: 'We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. The main idea is not to attempt to learn a single classifier that would have to work well across all occurring data distributions, nor many separate classifiers, but to exploit a hybrid strategy: we learn a single set of model parameters from which a specific classifier for any specific data distribution is derived via classifier adaptation. Assuming a multiclass classification setting with class-prior shift, the adaptation step can be performed analytically with only the classifier’s bias terms being affected. Another contribution of our work is an extrapolation step that predicts suitable adaptation parameters for future time steps based on the previous data. In combination, we obtain a lightweight procedure for learning from streaming data with varying class distribution that adds no trainable parameters and almost no memory or computational overhead compared to training a single model. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches, especially with respect to the relevant real-world metric of lowest within-day accuracy.' article_processing_charge: No author: - first_name: Paulina full_name: Tomaszewska, Paulina last_name: Tomaszewska - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Tomaszewska P, Lampert C. Lightweight conditional model extrapolation for streaming data under class-prior shift. In: 26th International Conference on Pattern Recognition. Vol 2022. Institute of Electrical and Electronics Engineers; 2022:2128-2134. doi:10.1109/icpr56361.2022.9956195' apa: 'Tomaszewska, P., & Lampert, C. (2022). Lightweight conditional model extrapolation for streaming data under class-prior shift. In 26th International Conference on Pattern Recognition (Vol. 2022, pp. 2128–2134). Montreal, Canada: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icpr56361.2022.9956195' chicago: Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.” In 26th International Conference on Pattern Recognition, 2022:2128–34. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/icpr56361.2022.9956195. ieee: P. Tomaszewska and C. Lampert, “Lightweight conditional model extrapolation for streaming data under class-prior shift,” in 26th International Conference on Pattern Recognition, Montreal, Canada, 2022, vol. 2022, pp. 2128–2134. ista: 'Tomaszewska P, Lampert C. 2022. Lightweight conditional model extrapolation for streaming data under class-prior shift. 26th International Conference on Pattern Recognition. ICPR: International Conference on Pattern Recognition vol. 2022, 2128–2134.' mla: Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.” 26th International Conference on Pattern Recognition, vol. 2022, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–34, doi:10.1109/icpr56361.2022.9956195. short: P. Tomaszewska, C. Lampert, in:, 26th International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–2134. conference: end_date: 2022-08-25 location: Montreal, Canada name: 'ICPR: International Conference on Pattern Recognition' start_date: 2022-08-21 date_created: 2023-01-12T12:09:38Z date_published: 2022-11-29T00:00:00Z date_updated: 2023-08-04T09:06:34Z day: '29' department: - _id: ChLa doi: 10.1109/icpr56361.2022.9956195 external_id: arxiv: - '2206.05181' isi: - '000897707602018' intvolume: ' 2022' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2206.05181 month: '11' oa: 1 oa_version: Preprint page: 2128-2134 publication: 26th International Conference on Pattern Recognition publication_identifier: eisbn: - '9781665490627' eissn: - 2831-7475 publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: Lightweight conditional model extrapolation for streaming data under class-prior shift type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 2022 year: '2022' ... --- _id: '10802' abstract: - lang: eng text: "Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. While many approaches have been developed for training fair models from data, little is known about the robustness of these methods to data corruption. In this work we consider fairness-aware learning under worst-case data manipulations. We show that an adversary can in some situations force any learner to return an overly biased classifier, regardless of the sample size and with or without degrading\r\naccuracy, and that the strength of the excess bias increases for learning problems with underrepresented protected groups in the data. We also prove that our hardness results are tight up to constant factors. To this end, we study two natural learning algorithms that optimize for both accuracy and fairness and show that these algorithms enjoy guarantees that are order-optimal in terms of the corruption ratio and the protected groups frequencies in the large data\r\nlimit." acknowledgement: The authors thank Eugenia Iofinova and Bernd Prach for providing feedback on early versions of this paper. This publication was made possible by an ETH AI Center postdoctoral fellowship to Nikola Konstantinov. article_processing_charge: No article_type: original author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0002-4561-241X citation: ama: Konstantinov NH, Lampert C. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 2022;23:1-60. apa: Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press. chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research. ML Research Press, 2022. ieee: N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted data,” Journal of Machine Learning Research, vol. 23. ML Research Press, pp. 1–60, 2022. ista: Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 23, 1–60. mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research, vol. 23, ML Research Press, 2022, pp. 1–60. short: N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60. date_created: 2022-02-28T14:05:42Z date_published: 2022-05-01T00:00:00Z date_updated: 2023-09-26T10:44:37Z day: '01' ddc: - '004' department: - _id: ChLa external_id: arxiv: - '2102.06004' file: - access_level: open_access checksum: 9cac897b54a0ddf3a553a2c33e88cfda content_type: application/pdf creator: kschuh date_created: 2022-07-12T15:08:28Z date_updated: 2022-07-12T15:08:28Z file_id: '11570' file_name: 2022_JournalMachineLearningResearch_Konstantinov.pdf file_size: 551862 relation: main_file success: 1 file_date_updated: 2022-07-12T15:08:28Z has_accepted_license: '1' intvolume: ' 23' keyword: - Fairness - robustness - data poisoning - trustworthy machine learning - PAC learning language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: 1-60 publication: Journal of Machine Learning Research publication_identifier: eissn: - 1533-7928 issn: - 1532-4435 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '10799' relation: dissertation_contains status: public - id: '13241' relation: shorter_version status: public scopus_import: '1' status: public title: Fairness-aware PAC learning from corrupted data tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 23 year: '2022' ... --- _id: '13241' abstract: - lang: eng text: Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. Many approaches for training fair models from data have been developed and an implicit assumption about such algorithms is that they are able to recover a fair model, despite potential historical biases in the data. In this work we show a number of impossibility results that indicate that there is no learning algorithm that can recover a fair model when a proportion of the dataset is subject to arbitrary manipulations. Specifically, we prove that there are situations in which an adversary can force any learner to return a biased classifier, with or without degrading accuracy, and that the strength of this bias increases for learning problems with underrepresented protected groups in the data. Our results emphasize on the importance of studying further data corruption models of various strength and of establishing stricter data collection practices for fairness-aware learning. acknowledgement: "This paper is a shortened, workshop version of Konstantinov and Lampert (2021),\r\nhttps://arxiv.org/abs/2102.06004. For further results, including an analysis of algorithms achieving the lower bounds from this paper, we refer to the full version." article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning from corrupted data. In: Proceedings of Machine Learning Research. Vol 171. ML Research Press; 2022:59-83.' apa: Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press. chicago: Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” In Proceedings of Machine Learning Research, 171:59–83. ML Research Press, 2022. ieee: N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83. ista: Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83. mla: Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” Proceedings of Machine Learning Research, vol. 171, ML Research Press, 2022, pp. 59–83. short: N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83. date_created: 2023-07-16T22:01:13Z date_published: 2022-12-01T00:00:00Z date_updated: 2023-09-26T10:44:37Z day: '01' department: - _id: ChLa external_id: arxiv: - '2102.06004' intvolume: ' 171' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2102.06004 month: '12' oa: 1 oa_version: Preprint page: 59-83 publication: Proceedings of Machine Learning Research publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '10802' relation: extended_version status: public scopus_import: '1' status: public title: On the impossibility of fairness-aware learning from corrupted data type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 171 year: '2022' ... --- _id: '9210' abstract: - lang: eng text: "Modern neural networks can easily fit their training set perfectly. Surprisingly, despite being “overfit” in this way, they tend to generalize well to future data, thereby defying the classic bias–variance trade-off of machine learning theory. Of the many possible explanations, a prevalent one is that training by stochastic gradient descent (SGD) imposes an implicit bias that leads it to learn simple functions, and these simple functions generalize well. However, the specifics of this implicit bias are not well understood.\r\nIn this work, we explore the smoothness conjecture which states that SGD is implicitly biased towards learning functions that are smooth. We propose several measures to formalize the intuitive notion of smoothness, and we conduct experiments to determine whether SGD indeed implicitly optimizes for these measures. Our findings rule out the possibility that smoothness measures based on first-order derivatives are being implicitly enforced. They are supportive, though, of the smoothness conjecture for measures based on second-order derivatives." article_processing_charge: No author: - first_name: Vaclav full_name: Volhejn, Vaclav id: d5235fb4-7a6d-11eb-b254-f25d12d631a8 last_name: Volhejn - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Volhejn V, Lampert C. Does SGD implicitly optimize for smoothness? In: 42nd German Conference on Pattern Recognition. Vol 12544. LNCS. Springer; 2021:246-259. doi:10.1007/978-3-030-71278-5_18' apa: 'Volhejn, V., & Lampert, C. (2021). Does SGD implicitly optimize for smoothness? In 42nd German Conference on Pattern Recognition (Vol. 12544, pp. 246–259). Tübingen, Germany: Springer. https://doi.org/10.1007/978-3-030-71278-5_18' chicago: Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for Smoothness?” In 42nd German Conference on Pattern Recognition, 12544:246–59. LNCS. Springer, 2021. https://doi.org/10.1007/978-3-030-71278-5_18. ieee: V. Volhejn and C. Lampert, “Does SGD implicitly optimize for smoothness?,” in 42nd German Conference on Pattern Recognition, Tübingen, Germany, 2021, vol. 12544, pp. 246–259. ista: 'Volhejn V, Lampert C. 2021. Does SGD implicitly optimize for smoothness? 42nd German Conference on Pattern Recognition. DAGM GCPR: German Conference on Pattern Recognition LNCS vol. 12544, 246–259.' mla: Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for Smoothness?” 42nd German Conference on Pattern Recognition, vol. 12544, Springer, 2021, pp. 246–59, doi:10.1007/978-3-030-71278-5_18. short: V. Volhejn, C. Lampert, in:, 42nd German Conference on Pattern Recognition, Springer, 2021, pp. 246–259. conference: end_date: 2020-10-01 location: Tübingen, Germany name: 'DAGM GCPR: German Conference on Pattern Recognition ' start_date: 2020-09-28 date_created: 2021-03-01T09:01:16Z date_published: 2021-03-17T00:00:00Z date_updated: 2022-08-12T07:28:47Z day: '17' ddc: - '510' department: - _id: ChLa doi: 10.1007/978-3-030-71278-5_18 file: - access_level: open_access checksum: 3e3628ab1cf658d82524963f808004ea content_type: application/pdf creator: dernst date_created: 2022-08-12T07:27:58Z date_updated: 2022-08-12T07:27:58Z file_id: '11820' file_name: 2020_GCPR_submitted_Volhejn.pdf file_size: 420234 relation: main_file success: 1 file_date_updated: 2022-08-12T07:27:58Z has_accepted_license: '1' intvolume: ' 12544' language: - iso: eng month: '03' oa: 1 oa_version: Submitted Version page: 246-259 publication: 42nd German Conference on Pattern Recognition publication_identifier: eissn: - 1611-3349 isbn: - '9783030712778' issn: - 0302-9743 publication_status: published publisher: Springer quality_controlled: '1' scopus_import: '1' series_title: LNCS status: public title: Does SGD implicitly optimize for smoothness? type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 12544 year: '2021' ... --- _id: '9416' abstract: - lang: eng text: 'We study the inductive bias of two-layer ReLU networks trained by gradient flow. We identify a class of easy-to-learn (`orthogonally separable'') datasets, and characterise the solution that ReLU networks trained on such datasets converge to. Irrespective of network width, the solution turns out to be a combination of two max-margin classifiers: one corresponding to the positive data subset and one corresponding to the negative data subset. The proof is based on the recently introduced concept of extremal sectors, for which we prove a number of properties in the context of orthogonal separability. In particular, we prove stationarity of activation patterns from some time onwards, which enables a reduction of the ReLU network to an ensemble of linear subnetworks.' article_processing_charge: No author: - first_name: Phuong full_name: Bui Thi Mai, Phuong id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87 last_name: Bui Thi Mai - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Phuong M, Lampert C. The inductive bias of ReLU networks on orthogonally separable data. In: 9th International Conference on Learning Representations. ; 2021.' apa: Phuong, M., & Lampert, C. (2021). The inductive bias of ReLU networks on orthogonally separable data. In 9th International Conference on Learning Representations. Virtual. chicago: Phuong, Mary, and Christoph Lampert. “The Inductive Bias of ReLU Networks on Orthogonally Separable Data.” In 9th International Conference on Learning Representations, 2021. ieee: M. Phuong and C. Lampert, “The inductive bias of ReLU networks on orthogonally separable data,” in 9th International Conference on Learning Representations, Virtual, 2021. ista: 'Phuong M, Lampert C. 2021. The inductive bias of ReLU networks on orthogonally separable data. 9th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Phuong, Mary, and Christoph Lampert. “The Inductive Bias of ReLU Networks on Orthogonally Separable Data.” 9th International Conference on Learning Representations, 2021. short: M. Phuong, C. Lampert, in:, 9th International Conference on Learning Representations, 2021. conference: end_date: 2021-05-07 location: Virtual name: ' ICLR: International Conference on Learning Representations' start_date: 2021-05-03 date_created: 2021-05-24T11:16:46Z date_published: 2021-05-01T00:00:00Z date_updated: 2023-09-07T13:29:50Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ChLa file: - access_level: open_access checksum: f34ff17017527db5ba6927f817bdd125 content_type: application/pdf creator: bphuong date_created: 2021-05-24T11:15:57Z date_updated: 2021-05-24T11:15:57Z file_id: '9417' file_name: iclr2021_conference.pdf file_size: 502356 relation: main_file file_date_updated: 2021-05-24T11:15:57Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/pdf?id=krz7T0xU9Z_ month: '05' oa: 1 oa_version: Published Version publication: 9th International Conference on Learning Representations publication_status: published quality_controlled: '1' related_material: record: - id: '9418' relation: dissertation_contains status: public scopus_import: '1' status: public title: The inductive bias of ReLU networks on orthogonally separable data type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '10803' abstract: - lang: eng text: Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users. Previous work on fair ranking has mostly focused on application-specific fairness notions, often tailored to online advertising, and it rarely considers learning as part of the process. In this work, we show how to transfer numerous fairness notions from binary classification to a learning to rank setting. Our formalism allows us to design methods for incorporating fairness objectives with provable generalization guarantees. An extensive experimental evaluation shows that our method can improve ranking fairness substantially with no or only little loss of model quality. article_number: '2102.05996' article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0002-4561-241X citation: ama: Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv. doi:10.48550/arXiv.2102.05996 apa: Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996 chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2102.05996. ieee: N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. . ista: Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv, 2102.05996. mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” ArXiv, 2102.05996, doi:10.48550/arXiv.2102.05996. short: N.H. Konstantinov, C. Lampert, ArXiv (n.d.). date_created: 2022-02-28T14:13:59Z date_published: 2021-06-07T00:00:00Z date_updated: 2023-09-07T13:42:08Z day: '07' department: - _id: ChLa doi: 10.48550/arXiv.2102.05996 external_id: arxiv: - '2102.05996' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2102.05996 month: '06' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted related_material: record: - id: '10799' relation: dissertation_contains status: public status: public title: Fairness through regularization for learning to rank type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14987' abstract: - lang: eng text: "The goal of zero-shot learning is to construct a classifier that can identify object classes for which no training examples are available. When training data for some of the object classes is available but not for others, the name generalized zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot is also used to describe other machine learning-based approaches that require no training data from the problem of interest, such as zero-shot action recognition or zero-shot machine translation." article_processing_charge: No author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. Computer Vision. 2nd ed. Cham: Springer; 2021:1395-1397. doi:10.1007/978-3-030-63416-2_874' apa: 'Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision (2nd ed., pp. 1395–1397). Cham: Springer. https://doi.org/10.1007/978-3-030-63416-2_874' chicago: 'Lampert, Christoph. “Zero-Shot Learning.” In Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63416-2_874.' ieee: 'C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.' ista: 'Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.' mla: Lampert, Christoph. “Zero-Shot Learning.” Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874. short: C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397. date_created: 2024-02-14T14:05:32Z date_published: 2021-10-13T00:00:00Z date_updated: 2024-02-19T10:59:04Z day: '13' department: - _id: ChLa doi: 10.1007/978-3-030-63416-2_874 edition: '2' editor: - first_name: Katsushi full_name: Ikeuchi, Katsushi last_name: Ikeuchi language: - iso: eng month: '10' oa_version: None page: 1395-1397 place: Cham publication: Computer Vision publication_identifier: eisbn: - '9783030634162' isbn: - '9783030634155' publication_status: published publisher: Springer quality_controlled: '1' status: public title: Zero-Shot Learning type: book_chapter user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '8063' abstract: - lang: eng text: "We present a generative model of images that explicitly reasons over the set\r\nof objects they show. Our model learns a structured latent representation that\r\nseparates objects from each other and from the background; unlike prior works,\r\nit explicitly represents the 2D position and depth of each object, as well as\r\nan embedding of its segmentation mask and appearance. The model can be trained\r\nfrom images alone in a purely unsupervised fashion without the need for object\r\nmasks or depth information. Moreover, it always generates complete objects,\r\neven though a significant fraction of training images contain occlusions.\r\nFinally, we show that our model can infer decompositions of novel images into\r\ntheir constituent objects, including accurate prediction of depth ordering and\r\nsegmentation of occluded parts." article_number: '2004.00642' article_processing_charge: No author: - first_name: Titas full_name: Anciukevicius, Titas last_name: Anciukevicius - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Paul M full_name: Henderson, Paul M id: 13C09E74-18D9-11E9-8878-32CFE5697425 last_name: Henderson orcid: 0000-0002-5198-7445 citation: ama: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. arXiv. apa: Anciukevicius, T., Lampert, C., & Henderson, P. M. (n.d.). Object-centric image generation with factored depths, locations, and appearances. arXiv. chicago: Anciukevicius, Titas, Christoph Lampert, and Paul M Henderson. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, n.d. ieee: T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” arXiv. . ista: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. arXiv, 2004.00642. mla: Anciukevicius, Titas, et al. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, 2004.00642. short: T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.). date_created: 2020-06-29T23:55:23Z date_published: 2020-04-01T00:00:00Z date_updated: 2021-01-12T08:16:44Z day: '01' ddc: - '004' department: - _id: ChLa external_id: arxiv: - '2004.00642' language: - iso: eng license: https://creativecommons.org/licenses/by-sa/4.0/ main_file_link: - open_access: '1' url: https://arxiv.org/abs/2004.00642 month: '04' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Object-centric image generation with factored depths, locations, and appearances tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '8188' abstract: - lang: eng text: "A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background, but without considering the underlying 3D scene that\r\ngives rise to them. We instead propose to model a video as the view seen while moving through a scene with multiple 3D objects and a 3D background. Our model is trained from monocular videos without any supervision, yet learns to\r\ngenerate coherent 3D scenes containing several moving objects. We conduct detailed experiments on two datasets, going beyond the visual complexity supported by state-of-the-art generative approaches. We evaluate our method on\r\ndepth-prediction and 3D object detection---tasks which cannot be addressed by those earlier works---and show it out-performs them even on 2D instance segmentation and tracking." acknowledged_ssus: - _id: ScienComp acknowledgement: "This research was supported by the Scientific Service Units (SSU) of IST Austria through resources\r\nprovided by Scientific Computing (SciComp). PH is employed part-time by Blackford Analysis, but\r\nthey did not support this project in any way." article_processing_charge: No author: - first_name: Paul M full_name: Henderson, Paul M id: 13C09E74-18D9-11E9-8878-32CFE5697425 last_name: Henderson orcid: 0000-0002-5198-7445 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Henderson PM, Lampert C. Unsupervised object-centric video generation and decomposition in 3D. In: 34th Conference on Neural Information Processing Systems. Vol 33. Curran Associates; 2020:3106–3117.' apa: 'Henderson, P. M., & Lampert, C. (2020). Unsupervised object-centric video generation and decomposition in 3D. In 34th Conference on Neural Information Processing Systems (Vol. 33, pp. 3106–3117). Vancouver, Canada: Curran Associates.' chicago: Henderson, Paul M, and Christoph Lampert. “Unsupervised Object-Centric Video Generation and Decomposition in 3D.” In 34th Conference on Neural Information Processing Systems, 33:3106–3117. Curran Associates, 2020. ieee: P. M. Henderson and C. Lampert, “Unsupervised object-centric video generation and decomposition in 3D,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 3106–3117. ista: 'Henderson PM, Lampert C. 2020. Unsupervised object-centric video generation and decomposition in 3D. 34th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 33, 3106–3117.' mla: Henderson, Paul M., and Christoph Lampert. “Unsupervised Object-Centric Video Generation and Decomposition in 3D.” 34th Conference on Neural Information Processing Systems, vol. 33, Curran Associates, 2020, pp. 3106–3117. short: P.M. Henderson, C. Lampert, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 3106–3117. conference: end_date: 2020-12-12 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2020-12-06 date_created: 2020-07-31T16:59:19Z date_published: 2020-07-07T00:00:00Z date_updated: 2023-04-25T09:49:58Z day: '07' department: - _id: ChLa external_id: arxiv: - '2007.06705' intvolume: ' 33' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2007.06705 month: '07' oa: 1 oa_version: Preprint page: 3106–3117 publication: 34th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713829546' publication_status: published publisher: Curran Associates quality_controlled: '1' status: public title: Unsupervised object-centric video generation and decomposition in 3D type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 33 year: '2020' ... --- _id: '7936' abstract: - lang: eng text: 'State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances and semantic categories. Orthogonal to this, many real-life object detection applications, for example in remote sensing, instead require dealing with large images that contain only a few small objects of a single class, scattered heterogeneously across the space. In addition, they are often subject to strict computational constraints, such as limited battery capacity and computing power.To tackle these more practical scenarios, we propose a novel flexible detection scheme that efficiently adapts to variable object sizes and densities: We rely on a sequence of detection stages, each of which has the ability to predict groups of objects as well as individuals. Similar to a detection cascade, this multi-stage architecture spares computational effort by discarding large irrelevant regions of the image early during the detection process. The ability to group objects provides further computational and memory savings, as it allows working with lower image resolutions in early stages, where groups are more easily detected than individuals, as they are more salient. We report experimental results on two aerial image datasets, and show that the proposed method is as accurate yet computationally more efficient than standard single-shot detectors, consistently across three different backbone architectures.' article_number: 1716-1725 article_processing_charge: No author: - first_name: Amélie full_name: Royer, Amélie id: 3811D890-F248-11E8-B48F-1D18A9856A87 last_name: Royer orcid: 0000-0002-8407-0705 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Royer A, Lampert C. Localizing grouped instances for efficient detection in low-resource scenarios. In: IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093288' apa: 'Royer, A., & Lampert, C. (2020). Localizing grouped instances for efficient detection in low-resource scenarios. In IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093288' chicago: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.” In IEEE Winter Conference on Applications of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093288. ieee: A. Royer and C. Lampert, “Localizing grouped instances for efficient detection in low-resource scenarios,” in IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, United States, 2020. ista: 'Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection in low-resource scenarios. IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.' mla: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.” IEEE Winter Conference on Applications of Computer Vision, 1716–1725, IEEE, 2020, doi:10.1109/WACV45572.2020.9093288. short: A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020. conference: end_date: 2020-03-05 location: ' Snowmass Village, CO, United States' name: 'WACV: Winter Conference on Applications of Computer Vision' start_date: 2020-03-01 date_created: 2020-06-07T22:00:53Z date_published: 2020-03-01T00:00:00Z date_updated: 2023-09-07T13:16:17Z day: '01' department: - _id: ChLa doi: 10.1109/WACV45572.2020.9093288 external_id: arxiv: - '2004.12623' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2004.12623 month: '03' oa: 1 oa_version: Preprint publication: IEEE Winter Conference on Applications of Computer Vision publication_identifier: isbn: - '9781728165530' publication_status: published publisher: IEEE quality_controlled: '1' related_material: record: - id: '8331' relation: dissertation_contains status: deleted - id: '8390' relation: dissertation_contains status: public scopus_import: 1 status: public title: Localizing grouped instances for efficient detection in low-resource scenarios type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '7937' abstract: - lang: eng text: 'Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained convolutional network for a new visual recognition task. However, the orthogonal setting of transferring knowledge from a pretrained network to a visually different yet semantically close source is rarely considered: This commonly happens with real-life data, which is not necessarily as clean as the training source (noise, geometric transformations, different modalities, etc.).To tackle such scenarios, we introduce a new, generalized form of fine-tuning, called flex-tuning, in which any individual unit (e.g. layer) of a network can be tuned, and the most promising one is chosen automatically. In order to make the method appealing for practical use, we propose two lightweight and faster selection procedures that prove to be good approximations in practice. We study these selection criteria empirically across a variety of domain shifts and data scarcity scenarios, and show that fine-tuning individual units, despite its simplicity, yields very good results as an adaptation technique. As it turns out, in contrast to common practice, rather than the last fully-connected unit it is best to tune an intermediate or early one in many domain- shift scenarios, which is accurately detected by flex-tuning.' article_number: 2180-2189 article_processing_charge: No author: - first_name: Amélie full_name: Royer, Amélie id: 3811D890-F248-11E8-B48F-1D18A9856A87 last_name: Royer orcid: 0000-0002-8407-0705 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer learning. In: 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093635' apa: 'Royer, A., & Lampert, C. (2020). A flexible selection scheme for minimum-effort transfer learning. In 2020 IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093635' chicago: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.” In 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093635. ieee: A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, United States, 2020. ista: 'Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 2180–2189.' mla: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.” 2020 IEEE Winter Conference on Applications of Computer Vision, 2180–2189, IEEE, 2020, doi:10.1109/WACV45572.2020.9093635. short: A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020. conference: end_date: 2020-03-05 location: Snowmass Village, CO, United States name: 'WACV: Winter Conference on Applications of Computer Vision' start_date: 2020-03-01 date_created: 2020-06-07T22:00:53Z date_published: 2020-03-01T00:00:00Z date_updated: 2023-09-07T13:16:17Z day: '01' department: - _id: ChLa doi: 10.1109/WACV45572.2020.9093635 external_id: arxiv: - '2008.11995' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/2008.11995 month: '03' oa: 1 oa_version: Preprint publication: 2020 IEEE Winter Conference on Applications of Computer Vision publication_identifier: isbn: - '9781728165530' publication_status: published publisher: IEEE quality_controlled: '1' related_material: record: - id: '8331' relation: dissertation_contains status: deleted - id: '8390' relation: dissertation_contains status: public scopus_import: '1' status: public title: A flexible selection scheme for minimum-effort transfer learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '7481' abstract: - lang: eng text: 'We address the following question: How redundant is the parameterisation of ReLU networks? Specifically, we consider transformations of the weight space which leave the function implemented by the network intact. Two such transformations are known for feed-forward architectures: permutation of neurons within a layer, and positive scaling of all incoming weights of a neuron coupled with inverse scaling of its outgoing weights. In this work, we show for architectures with non-increasing widths that permutation and scaling are in fact the only function-preserving weight transformations. For any eligible architecture we give an explicit construction of a neural network such that any other network that implements the same function can be obtained from the original one by the application of permutations and rescaling. The proof relies on a geometric understanding of boundaries between linear regions of ReLU networks, and we hope the developed mathematical tools are of independent interest.' article_processing_charge: No author: - first_name: Phuong full_name: Bui Thi Mai, Phuong id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87 last_name: Bui Thi Mai - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Phuong M, Lampert C. Functional vs. parametric equivalence of ReLU networks. In: 8th International Conference on Learning Representations. ; 2020.' apa: Phuong, M., & Lampert, C. (2020). Functional vs. parametric equivalence of ReLU networks. In 8th International Conference on Learning Representations. Online. chicago: Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence of ReLU Networks.” In 8th International Conference on Learning Representations, 2020. ieee: M. Phuong and C. Lampert, “Functional vs. parametric equivalence of ReLU networks,” in 8th International Conference on Learning Representations, Online, 2020. ista: 'Phuong M, Lampert C. 2020. Functional vs. parametric equivalence of ReLU networks. 8th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence of ReLU Networks.” 8th International Conference on Learning Representations, 2020. short: M. Phuong, C. Lampert, in:, 8th International Conference on Learning Representations, 2020. conference: end_date: 2020-04-30 location: Online name: 'ICLR: International Conference on Learning Representations' start_date: 2020-04-27 date_created: 2020-02-11T09:07:37Z date_published: 2020-04-26T00:00:00Z date_updated: 2023-09-07T13:29:50Z day: '26' ddc: - '000' department: - _id: ChLa file: - access_level: open_access checksum: 8d372ea5defd8cb8fdc430111ed754a9 content_type: application/pdf creator: bphuong date_created: 2020-02-11T09:07:27Z date_updated: 2020-07-14T12:47:59Z file_id: '7482' file_name: main.pdf file_size: 405469 relation: main_file file_date_updated: 2020-07-14T12:47:59Z has_accepted_license: '1' language: - iso: eng month: '04' oa: 1 oa_version: Published Version publication: 8th International Conference on Learning Representations publication_status: published quality_controlled: '1' related_material: link: - relation: supplementary_material url: https://iclr.cc/virtual_2020/poster_Bylx-TNKvH.html record: - id: '9418' relation: dissertation_contains status: public status: public title: Functional vs. parametric equivalence of ReLU networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '8724' abstract: - lang: eng text: "We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms. Specifically, we analyze the situation in which a learning system obtains datasets from multiple sources, some of which might be biased or even adversarially perturbed. It is\r\nknown that in the single-source case, an adversary with the power to corrupt a fixed fraction of the training data can prevent PAC-learnability, that is, even in the limit of infinitely much training data, no learning system can approach the optimal test error. In this work we show that, surprisingly, the same is not true in the multi-source setting, where the adversary can arbitrarily\r\ncorrupt a fixed fraction of the data sources. Our main results are a generalization bound that provides finite-sample guarantees for this learning setting, as well as corresponding lower bounds. Besides establishing PAC-learnability our results also show that in a cooperative learning setting sharing data with other parties has provable benefits, even if some\r\nparticipants are malicious. " acknowledged_ssus: - _id: ScienComp acknowledgement: Dan Alistarh is supported in part by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.' apa: 'Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.' chicago: Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020. ieee: N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425. ista: 'Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample complexity of adversarial multi-source PAC learning. Proceedings of the 37th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 119, 5416–5425.' mla: Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25. short: N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425. conference: end_date: 2020-07-18 location: Online name: 'ICML: International Conference on Machine Learning' start_date: 2020-07-12 date_created: 2020-11-05T15:25:58Z date_published: 2020-07-12T00:00:00Z date_updated: 2023-09-07T13:42:08Z day: '12' ddc: - '000' department: - _id: DaAl - _id: ChLa ec_funded: 1 external_id: arxiv: - '2002.10384' file: - access_level: open_access checksum: cc755d0054bc4b2be778ea7aa7884d2f content_type: application/pdf creator: dernst date_created: 2021-02-15T09:00:01Z date_updated: 2021-02-15T09:00:01Z file_id: '9120' file_name: 2020_PMLR_Konstantinov.pdf file_size: 281286 relation: main_file success: 1 file_date_updated: 2021-02-15T09:00:01Z has_accepted_license: '1' intvolume: ' 119' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 5416-5425 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: Proceedings of the 37th International Conference on Machine Learning publication_identifier: issn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: link: - relation: supplementary_material url: http://proceedings.mlr.press/v119/konstantinov20a/konstantinov20a-supp.pdf record: - id: '10799' relation: dissertation_contains status: public scopus_import: '1' status: public title: On the sample complexity of adversarial multi-source PAC learning type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 119 year: '2020' ... --- _id: '8186' abstract: - lang: eng text: "Numerous methods have been proposed for probabilistic generative modelling of\r\n3D objects. However, none of these is able to produce textured objects, which\r\nrenders them of limited use for practical tasks. In this work, we present the\r\nfirst generative model of textured 3D meshes. Training such a model would\r\ntraditionally require a large dataset of textured meshes, but unfortunately,\r\nexisting datasets of meshes lack detailed textures. We instead propose a new\r\ntraining methodology that allows learning from collections of 2D images without\r\nany 3D information. To do so, we train our model to explain a distribution of\r\nimages by modelling each image as a 3D foreground object placed in front of a\r\n2D background. Thus, it learns to generate meshes that when rendered, produce\r\nimages similar to those in its training set.\r\n A well-known problem when generating meshes with deep networks is the\r\nemergence of self-intersections, which are problematic for many use-cases. As a\r\nsecond contribution we therefore introduce a new generation process for 3D\r\nmeshes that guarantees no self-intersections arise, based on the physical\r\nintuition that faces should push one another out of the way as they move.\r\n We conduct extensive experiments on our approach, reporting quantitative and\r\nqualitative results on both synthetic data and natural images. These show our\r\nmethod successfully learns to generate plausible and diverse textured 3D\r\nsamples for five challenging object classes." article_processing_charge: No author: - first_name: Paul M full_name: Henderson, Paul M id: 13C09E74-18D9-11E9-8878-32CFE5697425 last_name: Henderson orcid: 0000-0002-5198-7445 - first_name: Vagia full_name: Tsiminaki, Vagia last_name: Tsiminaki - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Henderson PM, Tsiminaki V, Lampert C. Leveraging 2D data to learn textured 3D mesh generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2020:7498-7507. doi:10.1109/CVPR42600.2020.00752' apa: 'Henderson, P. M., Tsiminaki, V., & Lampert, C. (2020). Leveraging 2D data to learn textured 3D mesh generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7498–7507). Virtual: IEEE. https://doi.org/10.1109/CVPR42600.2020.00752' chicago: Henderson, Paul M, Vagia Tsiminaki, and Christoph Lampert. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7498–7507. IEEE, 2020. https://doi.org/10.1109/CVPR42600.2020.00752. ieee: P. M. Henderson, V. Tsiminaki, and C. Lampert, “Leveraging 2D data to learn textured 3D mesh generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, 2020, pp. 7498–7507. ista: 'Henderson PM, Tsiminaki V, Lampert C. 2020. Leveraging 2D data to learn textured 3D mesh generation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 7498–7507.' mla: Henderson, Paul M., et al. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–507, doi:10.1109/CVPR42600.2020.00752. short: P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507. conference: end_date: 2020-06-19 location: Virtual name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2020-06-14 date_created: 2020-07-31T16:53:49Z date_published: 2020-07-01T00:00:00Z date_updated: 2023-10-17T07:37:11Z day: '01' ddc: - '004' department: - _id: ChLa doi: 10.1109/CVPR42600.2020.00752 external_id: arxiv: - '2004.04180' file: - access_level: open_access content_type: application/pdf creator: phenders date_created: 2020-07-31T16:57:12Z date_updated: 2020-07-31T16:57:12Z file_id: '8187' file_name: paper.pdf file_size: 10262773 relation: main_file success: 1 file_date_updated: 2020-07-31T16:57:12Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://openaccess.thecvf.com/content_CVPR_2020/papers/Henderson_Leveraging_2D_Data_to_Learn_Textured_3D_Mesh_Generation_CVPR_2020_paper.pdf month: '07' oa: 1 oa_version: Submitted Version page: 7498-7507 publication: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_identifier: eisbn: - '9781728171685' eissn: - 2575-7075 publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Leveraging 2D data to learn textured 3D mesh generation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '6944' abstract: - lang: eng text: 'We study the problem of automatically detecting if a given multi-class classifier operates outside of its specifications (out-of-specs), i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier’s predictions cannot be guaranteed if it operates out-of-specs. Previously proposed methods for out-of-specs detection make decisions on the level of single inputs. This, however, is insufficient to achieve low false positive rate and high false negative rates at the same time. In this work, we describe a new procedure named KS(conf), based on statistical reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied to the set of predicted confidence values for batches of samples. Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change.' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Rémy full_name: Sun, Rémy last_name: Sun - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. 2020;128(4):970-995. doi:10.1007/s11263-019-01232-x' apa: 'Sun, R., & Lampert, C. (2020). KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01232-x' chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass Classifier Operates Outside of Its Specifications.” International Journal of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01232-x.' ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications,” International Journal of Computer Vision, vol. 128, no. 4. Springer Nature, pp. 970–995, 2020.' ista: 'Sun R, Lampert C. 2020. KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. 128(4), 970–995.' mla: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass Classifier Operates Outside of Its Specifications.” International Journal of Computer Vision, vol. 128, no. 4, Springer Nature, 2020, pp. 970–95, doi:10.1007/s11263-019-01232-x.' short: R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995. date_created: 2019-10-14T09:14:28Z date_published: 2020-04-01T00:00:00Z date_updated: 2024-02-22T14:57:30Z day: '01' ddc: - '004' department: - _id: ChLa doi: 10.1007/s11263-019-01232-x ec_funded: 1 external_id: isi: - '000494406800001' file: - access_level: open_access checksum: 155e63edf664dcacb3bdc1c2223e606f content_type: application/pdf creator: dernst date_created: 2019-11-26T10:30:02Z date_updated: 2020-07-14T12:47:45Z file_id: '7110' file_name: 2019_IJCV_Sun.pdf file_size: 1715072 relation: main_file file_date_updated: 2020-07-14T12:47:45Z has_accepted_license: '1' intvolume: ' 128' isi: 1 issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version page: 970-995 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding - _id: B67AFEDC-15C9-11EA-A837-991A96BB2854 name: IST Austria Open Access Fund publication: International Journal of Computer Vision publication_identifier: eissn: - 1573-1405 issn: - 0920-5691 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: erratum url: https://doi.org/10.1007/s11263-019-01262-5 record: - id: '6482' relation: earlier_version status: public scopus_import: '1' status: public title: 'KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 128 year: '2020' ... --- _id: '7171' abstract: - lang: ger text: "Wissen Sie, was sich hinter künstlicher Intelligenz und maschinellem Lernen verbirgt? \r\nDieses Sachbuch erklärt Ihnen leicht verständlich und ohne komplizierte Formeln die grundlegenden Methoden und Vorgehensweisen des maschinellen Lernens. Mathematisches Vorwissen ist dafür nicht nötig. Kurzweilig und informativ illustriert Lisa, die Protagonistin des Buches, diese anhand von Alltagssituationen. \r\nEin Buch für alle, die in Diskussionen über Chancen und Risiken der aktuellen Entwicklung der künstlichen Intelligenz und des maschinellen Lernens mit Faktenwissen punkten möchten. Auch für Schülerinnen und Schüler geeignet!" article_processing_charge: No citation: ama: 'Kersting K, Lampert C, Rothkopf C, eds. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden: Springer Nature; 2019. doi:10.1007/978-3-658-26763-6' apa: 'Kersting, K., Lampert, C., & Rothkopf, C. (Eds.). (2019). Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt (1st ed.). Wiesbaden: Springer Nature. https://doi.org/10.1007/978-3-658-26763-6' chicago: 'Kersting, Kristian, Christoph Lampert, and Constantin Rothkopf, eds. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden: Springer Nature, 2019. https://doi.org/10.1007/978-3-658-26763-6.' ieee: 'K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature, 2019.' ista: 'Kersting K, Lampert C, Rothkopf C eds. 2019. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt 1st ed., Wiesbaden: Springer Nature, XIV, 245p.' mla: 'Kersting, Kristian, et al., editors. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed., Springer Nature, 2019, doi:10.1007/978-3-658-26763-6.' short: 'K. Kersting, C. Lampert, C. Rothkopf, eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed., Springer Nature, Wiesbaden, 2019.' date_created: 2019-12-11T14:15:56Z date_published: 2019-10-30T00:00:00Z date_updated: 2021-12-22T14:40:58Z day: '30' department: - _id: ChLa doi: 10.1007/978-3-658-26763-6 edition: '1' editor: - first_name: Kristian full_name: Kersting, Kristian last_name: Kersting - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Constantin full_name: Rothkopf, Constantin last_name: Rothkopf language: - iso: ger month: '10' oa_version: None page: XIV, 245 place: Wiesbaden publication_identifier: eisbn: - 978-3-658-26763-6 isbn: - 978-3-658-26762-9 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - description: News on IST Website relation: press_release url: https://ist.ac.at/en/news/book-release-how-machines-learn/ status: public title: 'Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt' type: book_editor user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2019' ... --- _id: '6942' abstract: - lang: eng text: "Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of \U0001D714 -regular winning conditions; e.g., safety, reachability, liveness, parity conditions; provides a robust and expressive specification formalism for properties that arise in analysis of reactive systems. The resolutions of nondeterminism in games and MDPs are represented as strategies, and we consider succinct representation of such strategies. The decision-tree data structure from machine learning retains the flavor of decisions of strategies and allows entropy-based minimization to obtain succinct trees. However, in contrast to traditional machine-learning problems where small errors are allowed, for winning strategies in graph games and MDPs no error is allowed, and the decision tree must represent the entire strategy. In this work we propose decision trees with linear classifiers for representation of strategies in graph games and MDPs. We have implemented strategy representation using this data structure and we present experimental results for problems on graph games and MDPs, which show that this new data structure presents a much more efficient strategy representation as compared to standard decision trees." alternative_title: - LNCS article_processing_charge: No author: - first_name: Pranav full_name: Ashok, Pranav last_name: Ashok - first_name: Tomáš full_name: Brázdil, Tomáš last_name: Brázdil - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Jan full_name: Křetínský, Jan last_name: Křetínský - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Viktor full_name: Toman, Viktor id: 3AF3DA7C-F248-11E8-B48F-1D18A9856A87 last_name: Toman orcid: 0000-0001-9036-063X citation: ama: 'Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. Strategy representation by decision trees with linear classifiers. In: 16th International Conference on Quantitative Evaluation of Systems. Vol 11785. Springer Nature; 2019:109-128. doi:10.1007/978-3-030-30281-8_7' apa: 'Ashok, P., Brázdil, T., Chatterjee, K., Křetínský, J., Lampert, C., & Toman, V. (2019). Strategy representation by decision trees with linear classifiers. In 16th International Conference on Quantitative Evaluation of Systems (Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature. https://doi.org/10.1007/978-3-030-30281-8_7' chicago: Ashok, Pranav, Tomáš Brázdil, Krishnendu Chatterjee, Jan Křetínský, Christoph Lampert, and Viktor Toman. “Strategy Representation by Decision Trees with Linear Classifiers.” In 16th International Conference on Quantitative Evaluation of Systems, 11785:109–28. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-30281-8_7. ieee: P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, and V. Toman, “Strategy representation by decision trees with linear classifiers,” in 16th International Conference on Quantitative Evaluation of Systems, Glasgow, United Kingdom, 2019, vol. 11785, pp. 109–128. ista: 'Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. 2019. Strategy representation by decision trees with linear classifiers. 16th International Conference on Quantitative Evaluation of Systems. QEST: Quantitative Evaluation of Systems, LNCS, vol. 11785, 109–128.' mla: Ashok, Pranav, et al. “Strategy Representation by Decision Trees with Linear Classifiers.” 16th International Conference on Quantitative Evaluation of Systems, vol. 11785, Springer Nature, 2019, pp. 109–28, doi:10.1007/978-3-030-30281-8_7. short: P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, V. Toman, in:, 16th International Conference on Quantitative Evaluation of Systems, Springer Nature, 2019, pp. 109–128. conference: end_date: 2019-09-12 location: Glasgow, United Kingdom name: 'QEST: Quantitative Evaluation of Systems' start_date: 2019-09-10 date_created: 2019-10-14T06:57:49Z date_published: 2019-09-04T00:00:00Z date_updated: 2023-08-30T06:59:36Z day: '04' department: - _id: KrCh - _id: ChLa doi: 10.1007/978-3-030-30281-8_7 external_id: arxiv: - '1906.08178' isi: - '000679281300007' intvolume: ' 11785' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1906.08178 month: '09' oa: 1 oa_version: Preprint page: 109-128 project: - _id: 25863FF4-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S11407 name: Game Theory - _id: 25F2ACDE-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S11402-N23 name: Rigorous Systems Engineering - _id: 25892FC0-B435-11E9-9278-68D0E5697425 grant_number: ICT15-003 name: Efficient Algorithms for Computer Aided Verification publication: 16th International Conference on Quantitative Evaluation of Systems publication_identifier: eisbn: - '9783030302818' isbn: - '9783030302801' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Strategy representation by decision trees with linear classifiers type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 11785 year: '2019' ... --- _id: '6554' abstract: - lang: eng text: Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it. article_processing_charge: No article_type: original author: - first_name: Yongqin full_name: Xian, Yongqin last_name: Xian - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0002-4561-241X - first_name: Bernt full_name: Schiele, Bernt last_name: Schiele - first_name: Zeynep full_name: Akata, Zeynep last_name: Akata citation: ama: Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2019;41(9):2251-2265. doi:10.1109/tpami.2018.2857768 apa: Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2019). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tpami.2018.2857768 chicago: Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE), 2019. https://doi.org/10.1109/tpami.2018.2857768. ieee: Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9. Institute of Electrical and Electronics Engineers (IEEE), pp. 2251–2265, 2019. ista: Xian Y, Lampert C, Schiele B, Akata Z. 2019. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(9), 2251–2265. mla: Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9, Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 2251–65, doi:10.1109/tpami.2018.2857768. short: Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2019) 2251–2265. date_created: 2019-06-11T14:05:59Z date_published: 2019-09-01T00:00:00Z date_updated: 2023-09-05T13:18:09Z day: '01' department: - _id: ChLa doi: 10.1109/tpami.2018.2857768 external_id: arxiv: - '1707.00600' isi: - '000480343900015' intvolume: ' 41' isi: 1 issue: '9' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1707.00600 month: '09' oa: 1 oa_version: Preprint page: 2251 - 2265 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_identifier: eissn: - 1939-3539 issn: - 0162-8828 publication_status: published publisher: Institute of Electrical and Electronics Engineers (IEEE) quality_controlled: '1' scopus_import: '1' status: public title: Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 41 year: '2019' ... --- _id: '7479' abstract: - lang: eng text: "Multi-exit architectures, in which a stack of processing layers is interleaved with early output layers, allow the processing of a test example to stop early and thus save computation time and/or energy. In this work, we propose a new training procedure for multi-exit architectures based on the principle of knowledge distillation. The method encourage searly exits to mimic later, more accurate exits, by matching their output probabilities.\r\nExperiments on CIFAR100 and \ ImageNet show that distillation-based training significantly improves the accuracy of early exits while maintaining state-of-the-art accuracy for late \ ones. The method is particularly beneficial when training data is limited \ and it allows a straightforward extension to semi-supervised learning,i.e. making use of unlabeled data at training time. Moreover, it takes only afew lines to implement and incurs almost no computational overhead at training time, and none at all at test time." article_processing_charge: No author: - first_name: Phuong full_name: Bui Thi Mai, Phuong id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87 last_name: Bui Thi Mai - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Phuong M, Lampert C. Distillation-based training for multi-exit architectures. In: IEEE International Conference on Computer Vision. Vol 2019-October. IEEE; 2019:1355-1364. doi:10.1109/ICCV.2019.00144' apa: 'Phuong, M., & Lampert, C. (2019). Distillation-based training for multi-exit architectures. In IEEE International Conference on Computer Vision (Vol. 2019–October, pp. 1355–1364). Seoul, Korea: IEEE. https://doi.org/10.1109/ICCV.2019.00144' chicago: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit Architectures.” In IEEE International Conference on Computer Vision, 2019–October:1355–64. IEEE, 2019. https://doi.org/10.1109/ICCV.2019.00144. ieee: M. Phuong and C. Lampert, “Distillation-based training for multi-exit architectures,” in IEEE International Conference on Computer Vision, Seoul, Korea, 2019, vol. 2019–October, pp. 1355–1364. ista: 'Phuong M, Lampert C. 2019. Distillation-based training for multi-exit architectures. IEEE International Conference on Computer Vision. ICCV: International Conference on Computer Vision vol. 2019–October, 1355–1364.' mla: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit Architectures.” IEEE International Conference on Computer Vision, vol. 2019–October, IEEE, 2019, pp. 1355–64, doi:10.1109/ICCV.2019.00144. short: M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision, IEEE, 2019, pp. 1355–1364. conference: end_date: 2019-11-02 location: Seoul, Korea name: 'ICCV: International Conference on Computer Vision' start_date: 2019-10-27 date_created: 2020-02-11T09:06:57Z date_published: 2019-10-01T00:00:00Z date_updated: 2023-09-08T11:11:12Z day: '01' ddc: - '000' department: - _id: ChLa doi: 10.1109/ICCV.2019.00144 ec_funded: 1 external_id: isi: - '000531438101047' file: - access_level: open_access checksum: 7b77fb5c2d27c4c37a7612ba46a66117 content_type: application/pdf creator: bphuong date_created: 2020-02-11T09:06:39Z date_updated: 2020-07-14T12:47:59Z file_id: '7480' file_name: main.pdf file_size: 735768 relation: main_file file_date_updated: 2020-07-14T12:47:59Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '10' oa: 1 oa_version: Submitted Version page: 1355-1364 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: IEEE International Conference on Computer Vision publication_identifier: isbn: - '9781728148038' issn: - '15505499' publication_status: published publisher: IEEE quality_controlled: '1' related_material: record: - id: '9418' relation: dissertation_contains status: public scopus_import: '1' status: public title: Distillation-based training for multi-exit architectures type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2019-October year: '2019' ... --- _id: '7640' abstract: - lang: eng text: We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table". This task is an important step towards comprehensive structured mage understanding, going beyond detecting individual objects. Our main novelty is a Box Attention mechanism that allows to model pairwise interactions between objects using standard object detection pipelines. The resulting model is conceptually clean, expressive and relies on well-justified training and prediction procedures. Moreover, unlike previously proposed approaches, our model does not introduce any additional complex components or hyperparameters on top of those already required by the underlying detection model. We conduct an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating strong quantitative and qualitative results. article_number: 1749-1753 article_processing_charge: No author: - first_name: Alexander full_name: Kolesnikov, Alexander id: 2D157DB6-F248-11E8-B48F-1D18A9856A87 last_name: Kolesnikov - first_name: Alina full_name: Kuznetsova, Alina last_name: Kuznetsova - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Vittorio full_name: Ferrari, Vittorio last_name: Ferrari citation: ama: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. Detecting visual relationships using box attention. In: Proceedings of the 2019 International Conference on Computer Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00217' apa: 'Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting visual relationships using box attention. In Proceedings of the 2019 International Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217' chicago: Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio Ferrari. “Detecting Visual Relationships Using Box Attention.” In Proceedings of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019. https://doi.org/10.1109/ICCVW.2019.00217. ieee: A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual relationships using box attention,” in Proceedings of the 2019 International Conference on Computer Vision Workshop, Seoul, South Korea, 2019. ista: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. 2019. Detecting visual relationships using box attention. Proceedings of the 2019 International Conference on Computer Vision Workshop. ICCVW: International Conference on Computer Vision Workshop, 1749–1753.' mla: Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.” Proceedings of the 2019 International Conference on Computer Vision Workshop, 1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217. short: A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019. conference: end_date: 2019-10-28 location: Seoul, South Korea name: 'ICCVW: International Conference on Computer Vision Workshop' start_date: 2019-10-27 date_created: 2020-04-05T22:00:51Z date_published: 2019-10-01T00:00:00Z date_updated: 2023-09-08T11:18:37Z day: '01' department: - _id: ChLa doi: 10.1109/ICCVW.2019.00217 ec_funded: 1 external_id: arxiv: - '1807.02136' isi: - '000554591601098' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1807.02136 month: '10' oa: 1 oa_version: Preprint project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: Proceedings of the 2019 International Conference on Computer Vision Workshop publication_identifier: isbn: - '9781728150239' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Detecting visual relationships using box attention type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2019' ... --- _id: '6569' abstract: - lang: eng text: 'Knowledge distillation, i.e. one classifier being trained on the outputs of another classifier, is an empirically very successful technique for knowledge transfer between classifiers. It has even been observed that classifiers learn much faster and more reliably if trained with the outputs of another classifier as soft labels, instead of from ground truth data. So far, however, there is no satisfactory theoretical explanation of this phenomenon. In this work, we provide the first insights into the working mechanisms of distillation by studying the special case of linear and deep linear classifiers. Specifically, we prove a generalization bound that establishes fast convergence of the expected risk of a distillation-trained linear classifier. From the bound and its proof we extract three keyfactors that determine the success of distillation: data geometry – geometric properties of the datadistribution, in particular class separation, has an immediate influence on the convergence speed of the risk; optimization bias– gradient descentoptimization finds a very favorable minimum of the distillation objective; and strong monotonicity– the expected risk of the student classifier always decreases when the size of the training set grows.' article_processing_charge: No author: - first_name: Phuong full_name: Bui Thi Mai, Phuong id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87 last_name: Bui Thi Mai - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Phuong M, Lampert C. Towards understanding knowledge distillation. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:5142-5151.' apa: 'Phuong, M., & Lampert, C. (2019). Towards understanding knowledge distillation. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 5142–5151). Long Beach, CA, United States: ML Research Press.' chicago: Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.” In Proceedings of the 36th International Conference on Machine Learning, 97:5142–51. ML Research Press, 2019. ieee: M. Phuong and C. Lampert, “Towards understanding knowledge distillation,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 5142–5151. ista: 'Phuong M, Lampert C. 2019. Towards understanding knowledge distillation. Proceedings of the 36th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 97, 5142–5151.' mla: Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, ML Research Press, 2019, pp. 5142–51. short: M. Phuong, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 5142–5151. conference: end_date: 2019-06-15 location: Long Beach, CA, United States name: 'ICML: International Conference on Machine Learning' start_date: 2019-06-10 date_created: 2019-06-20T18:23:03Z date_published: 2019-06-13T00:00:00Z date_updated: 2023-10-17T12:31:38Z day: '13' ddc: - '000' department: - _id: ChLa file: - access_level: open_access checksum: a66d00e2694d749250f8507f301320ca content_type: application/pdf creator: bphuong date_created: 2019-06-20T18:22:56Z date_updated: 2020-07-14T12:47:33Z file_id: '6570' file_name: paper.pdf file_size: 686432 relation: main_file file_date_updated: 2020-07-14T12:47:33Z has_accepted_license: '1' intvolume: ' 97' language: - iso: eng month: '06' oa: 1 oa_version: Published Version page: 5142-5151 publication: Proceedings of the 36th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Towards understanding knowledge distillation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 97 year: '2019' ... --- _id: '6590' abstract: - lang: eng text: 'Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization. ' article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Konstantinov NH, Lampert C. Robust learning from untrusted sources. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:3488-3498.' apa: 'Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.' chicago: Konstantinov, Nikola H, and Christoph Lampert. “Robust Learning from Untrusted Sources.” In Proceedings of the 36th International Conference on Machine Learning, 97:3488–98. ML Research Press, 2019. ieee: N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498. ista: 'Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources. Proceedings of the 36th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 97, 3488–3498.' mla: Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted Sources.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, ML Research Press, 2019, pp. 3488–98. short: N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498. conference: end_date: 2919-06-15 location: Long Beach, CA, USA name: 'ICML: International Conference on Machine Learning' start_date: 2019-06-10 date_created: 2019-06-27T14:18:23Z date_published: 2019-06-01T00:00:00Z date_updated: 2023-10-17T12:31:55Z day: '01' department: - _id: ChLa ec_funded: 1 external_id: arxiv: - '1901.10310' intvolume: ' 97' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1901.10310 month: '06' oa: 1 oa_version: Preprint page: 3488-3498 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: Proceedings of the 36th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '10799' relation: dissertation_contains status: public scopus_import: '1' status: public title: Robust learning from untrusted sources type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 97 year: '2019' ... --- _id: '6482' abstract: - lang: eng text: 'Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for days or even years as components of real-world commercial applications. A major open problem in this context, however, is quality control. Good classification performance can only be expected if systems run under the specific conditions, in particular data distributions, that they were trained for. Surprisingly, none of the currently used deep network architectures have a built-in functionality that could detect if a network operates on data from a distribution it was not trained for, such that potentially a warning to the human users could be triggered. In this work, we describe KS(conf), a procedure for detecting such outside of specifications (out-of-specs) operation, based on statistical testing of the network outputs. We show by extensive experiments using the ImageNet, AwA2 and DAVIS datasets on a variety of ConvNets architectures that KS(conf) reliably detects out-of-specs situations. It furthermore has a number of properties that make it a promising candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with all networks, including pretrained ones, and requires no a priori knowledge of how the data distribution could change. ' alternative_title: - LNCS article_processing_charge: No author: - first_name: Rémy full_name: Sun, Rémy last_name: Sun - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a ConvNet operates outside of Its specifications. In: Vol 11269. Springer Nature; 2019:244-259. doi:10.1007/978-3-030-12939-2_18' apa: 'Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-12939-2_18' chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a ConvNet Operates Outside of Its Specifications,” 11269:244–59. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-12939-2_18.' ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a ConvNet operates outside of Its specifications,” presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany, 2019, vol. 11269, pp. 244–259.' ista: 'Sun R, Lampert C. 2019. KS(conf): A light-weight test if a ConvNet operates outside of Its specifications. GCPR: Conference on Pattern Recognition, LNCS, vol. 11269, 244–259.' mla: 'Sun, Rémy, and Christoph Lampert. KS(Conf): A Light-Weight Test If a ConvNet Operates Outside of Its Specifications. Vol. 11269, Springer Nature, 2019, pp. 244–59, doi:10.1007/978-3-030-12939-2_18.' short: R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259. conference: end_date: 2018-10-12 location: Stuttgart, Germany name: 'GCPR: Conference on Pattern Recognition' start_date: 2018-10-09 date_created: 2019-05-24T09:48:36Z date_published: 2019-02-14T00:00:00Z date_updated: 2024-02-22T14:57:29Z day: '14' department: - _id: ChLa doi: 10.1007/978-3-030-12939-2_18 ec_funded: 1 external_id: arxiv: - '1804.04171' intvolume: ' 11269' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1804.04171 month: '02' oa: 1 oa_version: Preprint page: 244-259 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_identifier: eissn: - 1611-3349 isbn: - '9783030129385' - '9783030129392' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '6944' relation: later_version status: public scopus_import: '1' status: public title: 'KS(conf): A light-weight test if a ConvNet operates outside of Its specifications' type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 11269 year: '2019' ... --- _id: '321' abstract: - lang: eng text: The twelve papers in this special section focus on learning systems with shared information for computer vision and multimedia communication analysis. In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes containing a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with shared information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different levels of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. article_processing_charge: No article_type: original author: - first_name: Trevor full_name: Darrell, Trevor last_name: Darrell - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Nico full_name: Sebe, Nico last_name: Sebe - first_name: Ying full_name: Wu, Ying last_name: Wu - first_name: Yan full_name: Yan, Yan last_name: Yan citation: ama: Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(5):1029-1031. doi:10.1109/TPAMI.2018.2804998 apa: Darrell, T., Lampert, C., Sebe, N., Wu, Y., & Yan, Y. (2018). Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2018.2804998 chicago: Darrell, Trevor, Christoph Lampert, Nico Sebe, Ying Wu, and Yan Yan. “Guest Editors’ Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2018.2804998. ieee: T. Darrell, C. Lampert, N. Sebe, Y. Wu, and Y. Yan, “Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5. IEEE, pp. 1029–1031, 2018. ista: Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. 2018. Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(5), 1029–1031. mla: Darrell, Trevor, et al. “Guest Editors’ Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, IEEE, 2018, pp. 1029–31, doi:10.1109/TPAMI.2018.2804998. short: T. Darrell, C. Lampert, N. Sebe, Y. Wu, Y. Yan, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1029–1031. date_created: 2018-12-11T11:45:48Z date_published: 2018-05-01T00:00:00Z date_updated: 2023-09-11T14:07:54Z day: '01' ddc: - '000' department: - _id: ChLa doi: 10.1109/TPAMI.2018.2804998 external_id: isi: - '000428901200001' file: - access_level: open_access checksum: b19c75da06faf3291a3ca47dfa50ef63 content_type: application/pdf creator: dernst date_created: 2020-05-14T12:50:48Z date_updated: 2020-07-14T12:46:03Z file_id: '7835' file_name: 2018_IEEE_Darrell.pdf file_size: 141724 relation: main_file file_date_updated: 2020-07-14T12:46:03Z has_accepted_license: '1' intvolume: ' 40' isi: 1 issue: '5' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: 1029 - 1031 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_status: published publisher: IEEE publist_id: '7544' quality_controlled: '1' scopus_import: '1' status: public title: Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 40 year: '2018' ... --- _id: '10882' abstract: - lang: eng text: 'We introduce Intelligent Annotation Dialogs for bounding box annotation. We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding box in a minimal amount of time. Specifically, we consider two actions: box verification [34], where the annotator verifies a box generated by an object detector, and manual box drawing. We explore two kinds of agents, one based on predicting the probability that a box will be positively verified, and the other based on reinforcement learning. We demonstrate that (1) our agents are able to learn efficient annotation strategies in several scenarios, automatically adapting to the image difficulty, the desired quality of the boxes, and the detector strength; (2) in all scenarios the resulting annotation dialogs speed up annotation compared to manual box drawing alone and box verification alone, while also outperforming any fixed combination of verification and drawing in most scenarios; (3) in a realistic scenario where the detector is iteratively re-trained, our agents evolve a series of strategies that reflect the shifting trade-off between verification and drawing as the detector grows stronger.' article_processing_charge: No author: - first_name: Jasper full_name: Uijlings, Jasper last_name: Uijlings - first_name: Ksenia full_name: Konyushkova, Ksenia last_name: Konyushkova - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Vittorio full_name: Ferrari, Vittorio last_name: Ferrari citation: ama: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs for bounding box annotation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2018:9175-9184. doi:10.1109/cvpr.2018.00956' apa: 'Uijlings, J., Konyushkova, K., Lampert, C., & Ferrari, V. (2018). Learning intelligent dialogs for bounding box annotation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9175–9184). Salt Lake City, UT, United States: IEEE. https://doi.org/10.1109/cvpr.2018.00956' chicago: Uijlings, Jasper, Ksenia Konyushkova, Christoph Lampert, and Vittorio Ferrari. “Learning Intelligent Dialogs for Bounding Box Annotation.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9175–84. IEEE, 2018. https://doi.org/10.1109/cvpr.2018.00956. ieee: J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent dialogs for bounding box annotation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, United States, 2018, pp. 9175–9184. ista: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. 2018. Learning intelligent dialogs for bounding box annotation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVF: Conference on Computer Vision and Pattern Recognition, 9175–9184.' mla: Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–84, doi:10.1109/cvpr.2018.00956. short: J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184. conference: end_date: 2018-06-23 location: Salt Lake City, UT, United States name: 'CVF: Conference on Computer Vision and Pattern Recognition' start_date: 2018-06-18 date_created: 2022-03-18T12:45:09Z date_published: 2018-12-17T00:00:00Z date_updated: 2023-09-19T15:11:49Z day: '17' department: - _id: ChLa doi: 10.1109/cvpr.2018.00956 external_id: arxiv: - '1712.08087' isi: - '000457843609036' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.1712.08087' month: '12' oa: 1 oa_version: Preprint page: 9175-9184 publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_identifier: eissn: - 2575-7075 isbn: - '9781538664209' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Learning intelligent dialogs for bounding box annotation type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2018' ... --- _id: '6012' abstract: - lang: eng text: We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task. article_processing_charge: No author: - first_name: Subham full_name: Sahoo, Subham last_name: Sahoo - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Georg S full_name: Martius, Georg S id: 3A276B68-F248-11E8-B48F-1D18A9856A87 last_name: Martius citation: ama: 'Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:4442-4450.' apa: 'Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for extrapolation and control. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research Press.' chicago: Sahoo, Subham, Christoph Lampert, and Georg S Martius. “Learning Equations for Extrapolation and Control.” In Proceedings of the 35th International Conference on Machine Learning, 80:4442–50. ML Research Press, 2018. ieee: S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation and control,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450. ista: 'Sahoo S, Lampert C, Martius GS. 2018. Learning equations for extrapolation and control. Proceedings of the 35th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 80, 4442–4450.' mla: Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 4442–50. short: S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450. conference: end_date: 2018-07-15 location: Stockholm, Sweden name: 'ICML: International Conference on Machine Learning' start_date: 2018-07-10 date_created: 2019-02-14T15:21:07Z date_published: 2018-02-01T00:00:00Z date_updated: 2023-10-17T09:50:53Z day: '01' department: - _id: ChLa ec_funded: 1 external_id: arxiv: - '1806.07259' isi: - '000683379204058' intvolume: ' 80' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1806.07259 month: '02' oa: 1 oa_version: Preprint page: 4442-4450 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: Proceedings of the 35th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: link: - description: News on IST Homepage relation: press_release url: https://ist.ac.at/en/news/first-machine-learning-method-capable-of-accurate-extrapolation/ scopus_import: '1' status: public title: Learning equations for extrapolation and control type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 80 year: '2018' ... --- _id: '6011' abstract: - lang: eng text: 'We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to previous distribution-free algorithmic stability results for SGD which depend on the worst-case constants. By virtue of the data-dependent argument, our bounds provide new insights into learning with SGD on convex and non-convex problems. In the convex case, we show that the bound on the generalization error depends on the risk at the initialization point. In the non-convex case, we prove that the expected curvature of the objective function around the initialization point has crucial influence on the generalization error. In both cases, our results suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization. As a corollary, our results allow us to show optimistic generalization bounds that exhibit fast convergence rates for SGD subject to a vanishing empirical risk and low noise of stochastic gradient. ' article_processing_charge: No author: - first_name: Ilja full_name: Kuzborskij, Ilja last_name: Kuzborskij - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kuzborskij I, Lampert C. Data-dependent stability of stochastic gradient descent. In: Proceedings of the 35 Th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:2815-2824.' apa: 'Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic gradient descent. In Proceedings of the 35 th International Conference on Machine Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: ML Research Press.' chicago: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic Gradient Descent.” In Proceedings of the 35 Th International Conference on Machine Learning, 80:2815–24. ML Research Press, 2018. ieee: I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient descent,” in Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824. ista: 'Kuzborskij I, Lampert C. 2018. Data-dependent stability of stochastic gradient descent. Proceedings of the 35 th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 80, 2815–2824.' mla: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic Gradient Descent.” Proceedings of the 35 Th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 2815–24. short: I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference on Machine Learning, ML Research Press, 2018, pp. 2815–2824. conference: end_date: 2018-07-15 location: Stockholm, Sweden name: 'ICML: International Conference on Machine Learning' start_date: 2018-07-10 date_created: 2019-02-14T14:51:57Z date_published: 2018-02-01T00:00:00Z date_updated: 2023-10-17T09:51:13Z day: '01' department: - _id: ChLa ec_funded: 1 external_id: arxiv: - '1703.01678' isi: - '000683379202095' intvolume: ' 80' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1703.01678 month: '02' oa: 1 oa_version: Preprint page: 2815-2824 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: Proceedings of the 35 th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Data-dependent stability of stochastic gradient descent type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 80 year: '2018' ... --- _id: '6841' abstract: - lang: eng text: In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs. In the natural sciences, however, finding an interpretable function for a phenomenon is the prime goal as it allows to understand and generalize results. This paper proposes a novel type of function learning network, called equation learner (EQL), that can learn analytical expressions and is able to extrapolate to unseen domains. It is implemented as an end-to-end differentiable feed-forward network and allows for efficient gradient based training. Due to sparsity regularization concise interpretable expressions can be obtained. Often the true underlying source expression is identified. author: - first_name: Georg S full_name: Martius, Georg S id: 3A276B68-F248-11E8-B48F-1D18A9856A87 last_name: Martius - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Martius GS, Lampert C. Extrapolation and learning equations. In: 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. International Conference on Learning Representations; 2017.' apa: 'Martius, G. S., & Lampert, C. (2017). Extrapolation and learning equations. In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. Toulon, France: International Conference on Learning Representations.' chicago: Martius, Georg S, and Christoph Lampert. “Extrapolation and Learning Equations.” In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. International Conference on Learning Representations, 2017. ieee: G. S. Martius and C. Lampert, “Extrapolation and learning equations,” in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, Toulon, France, 2017. ista: 'Martius GS, Lampert C. 2017. Extrapolation and learning equations. 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. ICLR: International Conference on Learning Representations.' mla: Martius, Georg S., and Christoph Lampert. “Extrapolation and Learning Equations.” 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations, 2017. short: G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations, 2017. conference: end_date: 2017-04-26 location: Toulon, France name: 'ICLR: International Conference on Learning Representations' start_date: 2017-04-24 date_created: 2019-09-01T22:01:00Z date_published: 2017-02-21T00:00:00Z date_updated: 2021-01-12T08:09:17Z day: '21' department: - _id: ChLa ec_funded: 1 external_id: arxiv: - '1610.02995' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1610.02995 month: '02' oa: 1 oa_version: Preprint project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings publication_status: published publisher: International Conference on Learning Representations quality_controlled: '1' scopus_import: 1 status: public title: Extrapolation and learning equations type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2017' ... --- _id: '750' abstract: - lang: eng text: Modern communication technologies allow first responders to contact thousands of potential volunteers simultaneously for support during a crisis or disaster event. However, such volunteer efforts must be well coordinated and monitored, in order to offer an effective relief to the professionals. In this paper we extend earlier work on optimally assigning volunteers to selected landmark locations. In particular, we emphasize the aspect that obtaining good assignments requires not only advanced computational tools, but also a realistic measure of distance between volunteers and landmarks. Specifically, we propose the use of the Open Street Map (OSM) driving distance instead of he previously used flight distance. We find the OSM driving distance to be better aligned with the interests of volunteers and first responders. Furthermore, we show that relying on the flying distance leads to a substantial underestimation of the number of required volunteers, causing negative side effects in case of an actual crisis situation. author: - first_name: Jasmin full_name: Pielorz, Jasmin id: 49BC895A-F248-11E8-B48F-1D18A9856A87 last_name: Pielorz - first_name: Matthias full_name: Prandtstetter, Matthias last_name: Prandtstetter - first_name: Markus full_name: Straub, Markus last_name: Straub - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Pielorz J, Prandtstetter M, Straub M, Lampert C. Optimal geospatial volunteer allocation needs realistic distances. In: 2017 IEEE International Conference on Big Data. IEEE; 2017:3760-3763. doi:10.1109/BigData.2017.8258375' apa: 'Pielorz, J., Prandtstetter, M., Straub, M., & Lampert, C. (2017). Optimal geospatial volunteer allocation needs realistic distances. In 2017 IEEE International Conference on Big Data (pp. 3760–3763). Boston, MA, United States: IEEE. https://doi.org/10.1109/BigData.2017.8258375' chicago: Pielorz, Jasmin, Matthias Prandtstetter, Markus Straub, and Christoph Lampert. “Optimal Geospatial Volunteer Allocation Needs Realistic Distances.” In 2017 IEEE International Conference on Big Data, 3760–63. IEEE, 2017. https://doi.org/10.1109/BigData.2017.8258375. ieee: J. Pielorz, M. Prandtstetter, M. Straub, and C. Lampert, “Optimal geospatial volunteer allocation needs realistic distances,” in 2017 IEEE International Conference on Big Data, Boston, MA, United States, 2017, pp. 3760–3763. ista: Pielorz J, Prandtstetter M, Straub M, Lampert C. 2017. Optimal geospatial volunteer allocation needs realistic distances. 2017 IEEE International Conference on Big Data. Big Data, 3760–3763. mla: Pielorz, Jasmin, et al. “Optimal Geospatial Volunteer Allocation Needs Realistic Distances.” 2017 IEEE International Conference on Big Data, IEEE, 2017, pp. 3760–63, doi:10.1109/BigData.2017.8258375. short: J. Pielorz, M. Prandtstetter, M. Straub, C. Lampert, in:, 2017 IEEE International Conference on Big Data, IEEE, 2017, pp. 3760–3763. conference: end_date: 2017-12-14 location: Boston, MA, United States name: Big Data start_date: 2017-12-11 date_created: 2018-12-11T11:48:18Z date_published: 2017-12-01T00:00:00Z date_updated: 2021-01-12T08:13:55Z day: '01' department: - _id: ChLa doi: 10.1109/BigData.2017.8258375 language: - iso: eng month: '12' oa_version: None page: 3760 - 3763 publication: 2017 IEEE International Conference on Big Data publication_identifier: isbn: - 978-153862714-3 publication_status: published publisher: IEEE publist_id: '6906' quality_controlled: '1' scopus_import: 1 status: public title: Optimal geospatial volunteer allocation needs realistic distances type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2017' ... --- _id: '1000' abstract: - lang: eng text: 'We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables. Subsequently, we describe two new generative image models that exploit different image transformations as latent variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of our LatentPixelCNN models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models. ' acknowledgement: We thank Tim Salimans for spotting a mistake in our preliminary arXiv manuscript. This work was funded by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 308036. article_processing_charge: No author: - first_name: Alexander full_name: Kolesnikov, Alexander id: 2D157DB6-F248-11E8-B48F-1D18A9856A87 last_name: Kolesnikov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kolesnikov A, Lampert C. PixelCNN models with auxiliary variables for natural image modeling. In: 34th International Conference on Machine Learning. Vol 70. JMLR; 2017:1905-1914.' apa: 'Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables for natural image modeling. In 34th International Conference on Machine Learning (Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.' chicago: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary Variables for Natural Image Modeling.” In 34th International Conference on Machine Learning, 70:1905–14. JMLR, 2017. ieee: A. Kolesnikov and C. Lampert, “PixelCNN models with auxiliary variables for natural image modeling,” in 34th International Conference on Machine Learning, Sydney, Australia, 2017, vol. 70, pp. 1905–1914. ista: 'Kolesnikov A, Lampert C. 2017. PixelCNN models with auxiliary variables for natural image modeling. 34th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 70, 1905–1914.' mla: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary Variables for Natural Image Modeling.” 34th International Conference on Machine Learning, vol. 70, JMLR, 2017, pp. 1905–14. short: A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine Learning, JMLR, 2017, pp. 1905–1914. conference: end_date: 2017-08-11 location: Sydney, Australia name: 'ICML: International Conference on Machine Learning' start_date: 2017-08-06 date_created: 2018-12-11T11:49:37Z date_published: 2017-08-01T00:00:00Z date_updated: 2023-09-22T09:50:41Z day: '01' department: - _id: ChLa ec_funded: 1 external_id: arxiv: - '1612.08185' isi: - '000683309501102' has_accepted_license: '1' intvolume: ' 70' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1612.08185 month: '08' oa: 1 oa_version: Submitted Version page: 1905 - 1914 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: 34th International Conference on Machine Learning publication_identifier: isbn: - 978-151085514-4 publication_status: published publisher: JMLR publist_id: '6398' quality_controlled: '1' scopus_import: '1' status: public title: PixelCNN models with auxiliary variables for natural image modeling type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 70 year: '2017' ... --- _id: '998' abstract: - lang: eng text: 'A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail. ' article_processing_charge: No author: - first_name: Sylvestre Alvise full_name: Rebuffi, Sylvestre Alvise last_name: Rebuffi - first_name: Alexander full_name: Kolesnikov, Alexander id: 2D157DB6-F248-11E8-B48F-1D18A9856A87 last_name: Kolesnikov - first_name: Georg full_name: Sperl, Georg id: 4DD40360-F248-11E8-B48F-1D18A9856A87 last_name: Sperl - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:10.1109/CVPR.2017.587' apa: 'Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL: Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.587' chicago: 'Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.587.' ieee: 'S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, “iCaRL: Incremental classifier and representation learning,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.' ista: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. 2017. iCaRL: Incremental classifier and representation learning. CVPR: Computer Vision and Pattern Recognition vol. 2017, 5533–5542.' mla: 'Rebuffi, Sylvestre Alvise, et al. ICaRL: Incremental Classifier and Representation Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.' short: S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542. conference: end_date: 2017-07-26 location: Honolulu, HA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2017-07-21 date_created: 2018-12-11T11:49:37Z date_published: 2017-04-14T00:00:00Z date_updated: 2023-09-22T09:51:58Z day: '14' department: - _id: ChLa - _id: ChWo doi: 10.1109/CVPR.2017.587 ec_funded: 1 external_id: isi: - '000418371405066' intvolume: ' 2017' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1611.07725 month: '04' oa: 1 oa_version: Submitted Version page: 5533 - 5542 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_identifier: isbn: - 978-153860457-1 publication_status: published publisher: IEEE publist_id: '6400' quality_controlled: '1' scopus_import: '1' status: public title: 'iCaRL: Incremental classifier and representation learning' type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '911' abstract: - lang: eng text: We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution.We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset. article_processing_charge: No author: - first_name: Amélie full_name: Royer, Amélie id: 3811D890-F248-11E8-B48F-1D18A9856A87 last_name: Royer orcid: 0000-0002-8407-0705 - first_name: Alexander full_name: Kolesnikov, Alexander id: 2D157DB6-F248-11E8-B48F-1D18A9856A87 last_name: Kolesnikov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Royer A, Kolesnikov A, Lampert C. Probabilistic image colorization. In: BMVA Press; 2017:85.1-85.12. doi:10.5244/c.31.85' apa: 'Royer, A., Kolesnikov, A., & Lampert, C. (2017). Probabilistic image colorization (p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London, United Kingdom: BMVA Press. https://doi.org/10.5244/c.31.85' chicago: Royer, Amélie, Alexander Kolesnikov, and Christoph Lampert. “Probabilistic Image Colorization,” 85.1-85.12. BMVA Press, 2017. https://doi.org/10.5244/c.31.85. ieee: 'A. Royer, A. Kolesnikov, and C. Lampert, “Probabilistic image colorization,” presented at the BMVC: British Machine Vision Conference, London, United Kingdom, 2017, p. 85.1-85.12.' ista: 'Royer A, Kolesnikov A, Lampert C. 2017. Probabilistic image colorization. BMVC: British Machine Vision Conference, 85.1-85.12.' mla: Royer, Amélie, et al. Probabilistic Image Colorization. BMVA Press, 2017, p. 85.1-85.12, doi:10.5244/c.31.85. short: A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12. conference: end_date: 2017-09-07 location: London, United Kingdom name: 'BMVC: British Machine Vision Conference' start_date: 2017-09-04 date_created: 2018-12-11T11:49:09Z date_published: 2017-09-01T00:00:00Z date_updated: 2023-10-16T10:04:02Z day: '01' ddc: - '000' department: - _id: ChLa doi: 10.5244/c.31.85 ec_funded: 1 external_id: arxiv: - '1705.04258' file: - access_level: open_access content_type: application/pdf creator: dernst date_created: 2020-08-10T07:14:33Z date_updated: 2020-08-10T07:14:33Z file_id: '8224' file_name: 2017_BMVC_Royer.pdf file_size: 1625363 relation: main_file success: 1 file_date_updated: 2020-08-10T07:14:33Z has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: 85.1-85.12 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_identifier: eisbn: - 190172560X publication_status: published publisher: BMVA Press publist_id: '6532' quality_controlled: '1' related_material: record: - id: '8390' relation: dissertation_contains status: public scopus_import: '1' status: public title: Probabilistic image colorization type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2017' ... --- _id: '1108' abstract: - lang: eng text: In this work we study the learnability of stochastic processes with respect to the conditional risk, i.e. the existence of a learning algorithm that improves its next-step performance with the amount of observed data. We introduce a notion of pairwise discrepancy between conditional distributions at different times steps and show how certain properties of these discrepancies can be used to construct a successful learning algorithm. Our main results are two theorems that establish criteria for learnability for many classes of stochastic processes, including all special cases studied previously in the literature. alternative_title: - PMLR article_processing_charge: No author: - first_name: Alexander full_name: Zimin, Alexander id: 37099E9C-F248-11E8-B48F-1D18A9856A87 last_name: Zimin - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Zimin A, Lampert C. Learning theory for conditional risk minimization. In: Vol 54. ML Research Press; 2017:213-222.' apa: 'Zimin, A., & Lampert, C. (2017). Learning theory for conditional risk minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States: ML Research Press.' chicago: Zimin, Alexander, and Christoph Lampert. “Learning Theory for Conditional Risk Minimization,” 54:213–22. ML Research Press, 2017. ieee: 'A. Zimin and C. Lampert, “Learning theory for conditional risk minimization,” presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 213–222.' ista: 'Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization. AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54, 213–222.' mla: Zimin, Alexander, and Christoph Lampert. Learning Theory for Conditional Risk Minimization. Vol. 54, ML Research Press, 2017, pp. 213–22. short: A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222. conference: end_date: 2017-04-22 location: Fort Lauderdale, FL, United States name: 'AISTATS: Artificial Intelligence and Statistics' start_date: 2017-04-20 date_created: 2018-12-11T11:50:11Z date_published: 2017-04-01T00:00:00Z date_updated: 2023-10-17T10:01:12Z day: '01' department: - _id: ChLa ec_funded: 1 external_id: isi: - '000509368500024' intvolume: ' 54' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: http://proceedings.mlr.press/v54/zimin17a/zimin17a.pdf month: '04' oa: 1 oa_version: Submitted Version page: 213 - 222 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_status: published publisher: ML Research Press publist_id: '6261' quality_controlled: '1' status: public title: Learning theory for conditional risk minimization type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 54 year: '2017' ... --- _id: '999' abstract: - lang: eng text: 'In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data must be available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is provided. Consequently, to solve all tasks, information must be transferred between tasks with labels and tasks without labels. Focusing on an instance-based transfer method we analyze two variants of this setting: when the set of labeled tasks is fixed, and when it can be actively selected by the learner. We state and prove a generalization bound that covers both scenarios and derive from it an algorithm for making the choice of labeled tasks (in the active case) and for transferring information between the tasks in a principled way. We also illustrate the effectiveness of the algorithm on synthetic and real data. ' alternative_title: - PMLR article_processing_charge: No author: - first_name: Anastasia full_name: Pentina, Anastasia id: 42E87FC6-F248-11E8-B48F-1D18A9856A87 last_name: Pentina - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks. In: Vol 70. ML Research Press; 2017:2807-2816.' apa: 'Pentina, A., & Lampert, C. (2017). Multi-task learning with labeled and unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International Conference on Machine Learning, Sydney, Australia: ML Research Press.' chicago: Pentina, Anastasia, and Christoph Lampert. “Multi-Task Learning with Labeled and Unlabeled Tasks,” 70:2807–16. ML Research Press, 2017. ieee: 'A. Pentina and C. Lampert, “Multi-task learning with labeled and unlabeled tasks,” presented at the ICML: International Conference on Machine Learning, Sydney, Australia, 2017, vol. 70, pp. 2807–2816.' ista: 'Pentina A, Lampert C. 2017. Multi-task learning with labeled and unlabeled tasks. ICML: International Conference on Machine Learning, PMLR, vol. 70, 2807–2816.' mla: Pentina, Anastasia, and Christoph Lampert. Multi-Task Learning with Labeled and Unlabeled Tasks. Vol. 70, ML Research Press, 2017, pp. 2807–16. short: A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816. conference: end_date: 2017-08-11 location: Sydney, Australia name: 'ICML: International Conference on Machine Learning' start_date: 2017-08-06 date_created: 2018-12-11T11:49:37Z date_published: 2017-06-08T00:00:00Z date_updated: 2023-10-17T11:53:32Z day: '08' department: - _id: ChLa ec_funded: 1 external_id: isi: - '000683309502093' intvolume: ' 70' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1602.06518 month: '06' oa: 1 oa_version: Submitted Version page: 2807 - 2816 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_identifier: isbn: - '9781510855144' publication_status: published publisher: ML Research Press publist_id: '6399' quality_controlled: '1' scopus_import: '1' status: public title: Multi-task learning with labeled and unlabeled tasks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 70 year: '2017' ... --- _id: '1102' abstract: - lang: eng text: Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g. trains on tracks. We propose a method to overcome these failures by adding a very small amount of model-specific additional annotation. The main idea is to cluster a deep network\'s mid-level representations and assign object or distractor labels to each cluster. Experiments show substantially improved localization results on the challenging ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for semantic segmentation. acknowledgement: "This work was funded in parts by the European Research Council\r\nunder the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant\r\nagreement no 308036. We gratefully acknowledge the support of NVIDIA Corporation with\r\nthe donation of the GPUs used for this research." author: - first_name: Alexander full_name: Kolesnikov, Alexander id: 2D157DB6-F248-11E8-B48F-1D18A9856A87 last_name: Kolesnikov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kolesnikov A, Lampert C. Improving weakly-supervised object localization by micro-annotation. In: Proceedings of the British Machine Vision Conference 2016. Vol 2016-September. BMVA Press; 2016:92.1-92.12. doi:10.5244/C.30.92' apa: 'Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object localization by micro-annotation. In Proceedings of the British Machine Vision Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom: BMVA Press. https://doi.org/10.5244/C.30.92' chicago: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised Object Localization by Micro-Annotation.” In Proceedings of the British Machine Vision Conference 2016, 2016–September:92.1-92.12. BMVA Press, 2016. https://doi.org/10.5244/C.30.92. ieee: A. Kolesnikov and C. Lampert, “Improving weakly-supervised object localization by micro-annotation,” in Proceedings of the British Machine Vision Conference 2016, York, United Kingdom, 2016, vol. 2016–September, p. 92.1-92.12. ista: 'Kolesnikov A, Lampert C. 2016. Improving weakly-supervised object localization by micro-annotation. Proceedings of the British Machine Vision Conference 2016. BMVC: British Machine Vision Conference vol. 2016–September, 92.1-92.12.' mla: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised Object Localization by Micro-Annotation.” Proceedings of the British Machine Vision Conference 2016, vol. 2016–September, BMVA Press, 2016, p. 92.1-92.12, doi:10.5244/C.30.92. short: A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12. conference: end_date: 2016-09-22 location: York, United Kingdom name: 'BMVC: British Machine Vision Conference' start_date: 2016-09-19 date_created: 2018-12-11T11:50:09Z date_published: 2016-09-01T00:00:00Z date_updated: 2021-01-12T06:48:18Z day: '01' department: - _id: ChLa doi: 10.5244/C.30.92 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: http://www.bmva.org/bmvc/2016/papers/paper092/paper092.pdf month: '09' oa: 1 oa_version: Published Version page: 92.1-92.12 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: Proceedings of the British Machine Vision Conference 2016 publication_status: published publisher: BMVA Press publist_id: '6273' quality_controlled: '1' scopus_import: 1 status: public title: Improving weakly-supervised object localization by micro-annotation type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 2016-September year: '2016' ... --- _id: '1369' abstract: - lang: eng text: 'We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.' alternative_title: - LNCS author: - first_name: Alexander full_name: Kolesnikov, Alexander id: 2D157DB6-F248-11E8-B48F-1D18A9856A87 last_name: Kolesnikov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:10.1007/978-3-319-46493-0_42' apa: 'Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711). Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42' chicago: 'Kolesnikov, Alexander, and Christoph Lampert. “Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation,” 9908:695–711. Springer, 2016. https://doi.org/10.1007/978-3-319-46493-0_42.' ieee: 'A. Kolesnikov and C. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands, 2016, vol. 9908, pp. 695–711.' ista: 'Kolesnikov A, Lampert C. 2016. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. ECCV: European Conference on Computer Vision, LNCS, vol. 9908, 695–711.' mla: 'Kolesnikov, Alexander, and Christoph Lampert. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation. Vol. 9908, Springer, 2016, pp. 695–711, doi:10.1007/978-3-319-46493-0_42.' short: A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711. conference: end_date: 2016-10-14 location: Amsterdam, The Netherlands name: 'ECCV: European Conference on Computer Vision' start_date: 2016-10-11 date_created: 2018-12-11T11:51:37Z date_published: 2016-09-15T00:00:00Z date_updated: 2021-01-12T06:50:12Z day: '15' department: - _id: ChLa doi: 10.1007/978-3-319-46493-0_42 ec_funded: 1 intvolume: ' 9908' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1603.06098 month: '09' oa: 1 oa_version: Preprint page: 695 - 711 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_status: published publisher: Springer publist_id: '5842' quality_controlled: '1' scopus_import: 1 status: public title: 'Seed, expand and constrain: Three principles for weakly-supervised image segmentation' type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 9908 year: '2016' ... --- _id: '1707' abstract: - lang: eng text: "Volunteer supporters play an important role in modern crisis and disaster management. In the times of mobile Internet devices, help from thousands of volunteers can be requested within a short time span, thus relieving professional helpers from minor chores or geographically spread-out tasks. However, the simultaneous availability of many volunteers also poses new problems. In particular, the volunteer efforts must be well coordinated, or otherwise situations might emerge in which too many idle volunteers at one location become more of a burden than a relief to the professionals.\r\nIn this work, we study the task of optimally assigning volunteers to selected locations, e.g. in order to perform regular measurements, to report on damage, or to distribute information or resources to the population in a crisis situation. We formulate the assignment tasks as an optimization problem and propose an effective and efficient solution procedure. Experiments on real data of the Team Österreich, consisting of over 36,000 Austrian volunteers, show the effectiveness and efficiency of our approach." acknowledgement: The DRIVER FP7 project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement no 607798. RE-ACTA was funded within the framework of the Austrian Security Research Programme KIRAS by the Federal Ministry for Transport, Innovation and Technology. article_number: '7402041' author: - first_name: Jasmin full_name: Pielorz, Jasmin id: 49BC895A-F248-11E8-B48F-1D18A9856A87 last_name: Pielorz - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Pielorz J, Lampert C. Optimal geospatial allocation of volunteers for crisis management. In: IEEE; 2016. doi:10.1109/ICT-DM.2015.7402041' apa: 'Pielorz, J., & Lampert, C. (2016). Optimal geospatial allocation of volunteers for crisis management. Presented at the ICT-DM: Information and Communication Technologies for Disaster Management, Rennes, France: IEEE. https://doi.org/10.1109/ICT-DM.2015.7402041' chicago: Pielorz, Jasmin, and Christoph Lampert. “Optimal Geospatial Allocation of Volunteers for Crisis Management.” IEEE, 2016. https://doi.org/10.1109/ICT-DM.2015.7402041. ieee: 'J. Pielorz and C. Lampert, “Optimal geospatial allocation of volunteers for crisis management,” presented at the ICT-DM: Information and Communication Technologies for Disaster Management, Rennes, France, 2016.' ista: 'Pielorz J, Lampert C. 2016. Optimal geospatial allocation of volunteers for crisis management. ICT-DM: Information and Communication Technologies for Disaster Management, 7402041.' mla: Pielorz, Jasmin, and Christoph Lampert. Optimal Geospatial Allocation of Volunteers for Crisis Management. 7402041, IEEE, 2016, doi:10.1109/ICT-DM.2015.7402041. short: J. Pielorz, C. Lampert, in:, IEEE, 2016. conference: end_date: 2015-12-02 location: Rennes, France name: 'ICT-DM: Information and Communication Technologies for Disaster Management' start_date: 2015-11-30 date_created: 2018-12-11T11:53:35Z date_published: 2016-02-11T00:00:00Z date_updated: 2021-01-12T06:52:39Z day: '11' department: - _id: ChLa doi: 10.1109/ICT-DM.2015.7402041 language: - iso: eng month: '02' oa_version: None publication_status: published publisher: IEEE publist_id: '5429' quality_controlled: '1' scopus_import: 1 status: public title: Optimal geospatial allocation of volunteers for crisis management type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2016' ... --- _id: '1425' abstract: - lang: eng text: 'In this work we aim at extending the theoretical foundations of lifelong learning. Previous work analyzing this scenario is based on the assumption that learning tasks are sampled i.i.d. from a task environment or limited to strongly constrained data distributions. Instead, we study two scenarios when lifelong learning is possible, even though the observed tasks do not form an i.i.d. sample: first, when they are sampled from the same environment, but possibly with dependencies, and second, when the task environment is allowed to change over time in a consistent way. In the first case we prove a PAC-Bayesian theorem that can be seen as a direct generalization of the analogous previous result for the i.i.d. case. For the second scenario we propose to learn an inductive bias in form of a transfer procedure. We present a generalization bound and show on a toy example how it can be used to identify a beneficial transfer algorithm.' alternative_title: - Advances in Neural Information Processing Systems author: - first_name: Anastasia full_name: Pentina, Anastasia id: 42E87FC6-F248-11E8-B48F-1D18A9856A87 last_name: Pentina - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Pentina A, Lampert C. Lifelong learning with non-i.i.d. tasks. In: Vol 2015. Neural Information Processing Systems; 2015:1540-1548.' apa: 'Pentina, A., & Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks (Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.' chicago: Pentina, Anastasia, and Christoph Lampert. “Lifelong Learning with Non-i.i.d. Tasks,” 2015:1540–48. Neural Information Processing Systems, 2015. ieee: 'A. Pentina and C. Lampert, “Lifelong learning with non-i.i.d. tasks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol. 2015, pp. 1540–1548.' ista: 'Pentina A, Lampert C. 2015. Lifelong learning with non-i.i.d. tasks. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 2015, 1540–1548.' mla: Pentina, Anastasia, and Christoph Lampert. Lifelong Learning with Non-i.i.d. Tasks. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48. short: A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015, pp. 1540–1548. conference: end_date: 2015-12-12 location: Montreal, Canada name: 'NIPS: Neural Information Processing Systems' start_date: 2015-12-07 date_created: 2018-12-11T11:51:57Z date_published: 2015-01-01T00:00:00Z date_updated: 2021-01-12T06:50:39Z day: '01' department: - _id: ChLa ec_funded: 1 intvolume: ' 2015' language: - iso: eng main_file_link: - open_access: '1' url: http://papers.nips.cc/paper/6007-lifelong-learning-with-non-iid-tasks month: '01' oa: 1 oa_version: None page: 1540 - 1548 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_status: published publisher: Neural Information Processing Systems publist_id: '5781' quality_controlled: '1' scopus_import: 1 status: public title: Lifelong learning with non-i.i.d. tasks type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 2015 year: '2015' ... --- _id: '1859' abstract: - lang: eng text: "Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \\emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut.\r\nIn this work, we introduce a new algorithm for SSVM training that is more efficient than earlier techniques when the max-oracle is computationally expensive, as it is frequently the case in computer vision tasks. The main idea is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm with efficient hyperplane caching, and (ii) use an automatic selection rule for deciding whether to call the exact max-oracle or to rely on an approximate one based on the cached hyperplanes.\r\nWe show experimentally that this strategy leads to faster convergence to the optimum with respect to the number of requires oracle calls, and that this translates into faster convergence with respect to the total runtime when the max-oracle is slow compared to the other steps of the algorithm. " author: - first_name: Neel full_name: Shah, Neel id: 31ABAF80-F248-11E8-B48F-1D18A9856A87 last_name: Shah - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Shah N, Kolmogorov V, Lampert C. A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle. In: IEEE; 2015:2737-2745. doi:10.1109/CVPR.2015.7298890' apa: 'Shah, N., Kolmogorov, V., & Lampert, C. (2015). A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp. 2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7298890' chicago: Shah, Neel, Vladimir Kolmogorov, and Christoph Lampert. “A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle,” 2737–45. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298890. ieee: 'N. Shah, V. Kolmogorov, and C. Lampert, “A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 2737–2745.' ista: 'Shah N, Kolmogorov V, Lampert C. 2015. A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle. CVPR: Computer Vision and Pattern Recognition, 2737–2745.' mla: Shah, Neel, et al. A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle. IEEE, 2015, pp. 2737–45, doi:10.1109/CVPR.2015.7298890. short: N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745. conference: end_date: 2015-06-12 location: Boston, MA, USA name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2015-06-07 date_created: 2018-12-11T11:54:24Z date_published: 2015-06-01T00:00:00Z date_updated: 2021-01-12T06:53:40Z day: '01' department: - _id: VlKo - _id: ChLa doi: 10.1109/CVPR.2015.7298890 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1408.6804 month: '06' oa: 1 oa_version: Preprint page: 2737 - 2745 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_status: published publisher: IEEE publist_id: '5240' quality_controlled: '1' scopus_import: 1 status: public title: A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '1860' abstract: - lang: eng text: Classifiers for object categorization are usually evaluated by their accuracy on a set of i.i.d. test examples. This provides us with an estimate of the expected error when applying the classifiers to a single new image. In real application, however, classifiers are rarely only used for a single image and then discarded. Instead, they are applied sequentially to many images, and these are typically not i.i.d. samples from a fixed data distribution, but they carry dependencies and their class distribution varies over time. In this work, we argue that the phenomenon of correlated data at prediction time is not a nuisance, but a blessing in disguise. We describe a probabilistic method for adapting classifiers at prediction time without having to retrain them. We also introduce a framework for creating realistically distributed image sequences, which offers a way to benchmark classifier adaptation methods, such as the one we propose. Experiments on the ILSVRC2010 and ILSVRC2012 datasets show that adapting object classification systems at prediction time can significantly reduce their error rate, even with no additional human feedback. author: - first_name: Amélie full_name: Royer, Amélie last_name: Royer - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Royer A, Lampert C. Classifier adaptation at prediction time. In: IEEE; 2015:1401-1409. doi:10.1109/CVPR.2015.7298746' apa: 'Royer, A., & Lampert, C. (2015). Classifier adaptation at prediction time (pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298746' chicago: Royer, Amélie, and Christoph Lampert. “Classifier Adaptation at Prediction Time,” 1401–9. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298746. ieee: 'A. Royer and C. Lampert, “Classifier adaptation at prediction time,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 1401–1409.' ista: 'Royer A, Lampert C. 2015. Classifier adaptation at prediction time. CVPR: Computer Vision and Pattern Recognition, 1401–1409.' mla: Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746. short: A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409. conference: end_date: 2015-06-12 location: Boston, MA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2015-06-07 date_created: 2018-12-11T11:54:24Z date_published: 2015-06-01T00:00:00Z date_updated: 2021-01-12T06:53:41Z day: '01' department: - _id: ChLa doi: 10.1109/CVPR.2015.7298746 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Royer_Classifier_Adaptation_at_2015_CVPR_paper.pdf month: '06' oa: 1 oa_version: Submitted Version page: 1401 - 1409 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_status: published publisher: IEEE publist_id: '5239' quality_controlled: '1' scopus_import: 1 status: public title: Classifier adaptation at prediction time type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '1858' abstract: - lang: eng text: 'We study the problem of predicting the future, though only in the probabilistic sense of estimating a future state of a time-varying probability distribution. This is not only an interesting academic problem, but solving this extrapolation problem also has many practical application, e.g. for training classifiers that have to operate under time-varying conditions. Our main contribution is a method for predicting the next step of the time-varying distribution from a given sequence of sample sets from earlier time steps. For this we rely on two recent machine learning techniques: embedding probability distributions into a reproducing kernel Hilbert space, and learning operators by vector-valued regression. We illustrate the working principles and the practical usefulness of our method by experiments on synthetic and real data. We also highlight an exemplary application: training a classifier in a domain adaptation setting without having access to examples from the test time distribution at training time.' author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Lampert C. Predicting the future behavior of a time-varying probability distribution. In: IEEE; 2015:942-950. doi:10.1109/CVPR.2015.7298696' apa: 'Lampert, C. (2015). Predicting the future behavior of a time-varying probability distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298696' chicago: Lampert, Christoph. “Predicting the Future Behavior of a Time-Varying Probability Distribution,” 942–50. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298696. ieee: 'C. Lampert, “Predicting the future behavior of a time-varying probability distribution,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 942–950.' ista: 'Lampert C. 2015. Predicting the future behavior of a time-varying probability distribution. CVPR: Computer Vision and Pattern Recognition, 942–950.' mla: Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696. short: C. Lampert, in:, IEEE, 2015, pp. 942–950. conference: end_date: 2015-06-12 location: Boston, MA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2015-06-07 date_created: 2018-12-11T11:54:24Z date_published: 2015-10-15T00:00:00Z date_updated: 2021-01-12T06:53:40Z day: '15' department: - _id: ChLa doi: 10.1109/CVPR.2015.7298696 external_id: arxiv: - '1406.5362' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1406.5362 month: '10' oa: 1 oa_version: Preprint page: 942 - 950 publication_status: published publisher: IEEE publist_id: '5241' quality_controlled: '1' scopus_import: 1 status: public title: Predicting the future behavior of a time-varying probability distribution type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '1857' abstract: - lang: eng text: 'Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario of multi-task learning not all tasks are equally related to each other, hence it could be advantageous to transfer information only between the most related tasks. In this work we propose an approach that processes multiple tasks in a sequence with sharing between subsequent tasks instead of solving all tasks jointly. Subsequently, we address the question of curriculum learning of tasks, i.e. finding the best order of tasks to be learned. Our approach is based on a generalization bound criterion for choosing the task order that optimizes the average expected classification performance over all tasks. Our experimental results show that learning multiple related tasks sequentially can be more effective than learning them jointly, the order in which tasks are being solved affects the overall performance, and that our model is able to automatically discover the favourable order of tasks. ' author: - first_name: Anastasia full_name: Pentina, Anastasia id: 42E87FC6-F248-11E8-B48F-1D18A9856A87 last_name: Pentina - first_name: Viktoriia full_name: Sharmanska, Viktoriia id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87 last_name: Sharmanska orcid: 0000-0003-0192-9308 - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Pentina A, Sharmanska V, Lampert C. Curriculum learning of multiple tasks. In: IEEE; 2015:5492-5500. doi:10.1109/CVPR.2015.7299188' apa: 'Pentina, A., Sharmanska, V., & Lampert, C. (2015). Curriculum learning of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7299188' chicago: Pentina, Anastasia, Viktoriia Sharmanska, and Christoph Lampert. “Curriculum Learning of Multiple Tasks,” 5492–5500. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7299188. ieee: 'A. Pentina, V. Sharmanska, and C. Lampert, “Curriculum learning of multiple tasks,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 5492–5500.' ista: 'Pentina A, Sharmanska V, Lampert C. 2015. Curriculum learning of multiple tasks. CVPR: Computer Vision and Pattern Recognition, 5492–5500.' mla: Pentina, Anastasia, et al. Curriculum Learning of Multiple Tasks. IEEE, 2015, pp. 5492–500, doi:10.1109/CVPR.2015.7299188. short: A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500. conference: end_date: 2015-06-12 location: Boston, MA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2015-06-07 date_created: 2018-12-11T11:54:23Z date_published: 2015-06-01T00:00:00Z date_updated: 2023-02-23T10:17:31Z day: '01' department: - _id: ChLa doi: 10.1109/CVPR.2015.7299188 language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1412.1353 month: '06' oa: 1 oa_version: Preprint page: 5492 - 5500 publication_status: published publisher: IEEE publist_id: '5243' quality_controlled: '1' scopus_import: 1 status: public title: Curriculum learning of multiple tasks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '1829' abstract: - lang: eng text: Hitting and batting tasks, such as tennis forehands, ping-pong strokes, or baseball batting, depend on predictions where the ball can be intercepted and how it can properly be returned to the opponent. These predictions get more accurate over time, hence the behaviors need to be continuously modified. As a result, movement templates with a learned global shape need to be adapted during the execution so that the racket reaches a target position and velocity that will return the ball over to the other side of the net or court. It requires altering learned movements to hit a varying target with the necessary velocity at a specific instant in time. Such a task cannot be incorporated straightforwardly in most movement representations suitable for learning. For example, the standard formulation of the dynamical system based motor primitives (introduced by Ijspeert et al (2002b)) does not satisfy this property despite their flexibility which has allowed learning tasks ranging from locomotion to kendama. In order to fulfill this requirement, we reformulate the Ijspeert framework to incorporate the possibility of specifying a desired hitting point and a desired hitting velocity while maintaining all advantages of the original formulation.We show that the proposed movement template formulation works well in two scenarios, i.e., for hitting a ball on a string with a table tennis racket at a specified velocity and for returning balls launched by a ball gun successfully over the net using forehand movements. alternative_title: - Springer Tracts in Advanced Robotics author: - first_name: Katharina full_name: Muelling, Katharina last_name: Muelling - first_name: Oliver full_name: Kroemer, Oliver last_name: Kroemer - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Muelling K, Kroemer O, Lampert C, Schölkopf B. Movement templates for learning of hitting and batting. In: Kober J, Peters J, eds. Learning Motor Skills. Vol 97. From Algorithms to Robot Experiments. Springer; 2014:69-82. doi:10.1007/978-3-319-03194-1_3' apa: Muelling, K., Kroemer, O., Lampert, C., & Schölkopf, B. (2014). Movement templates for learning of hitting and batting. In J. Kober & J. Peters (Eds.), Learning Motor Skills (Vol. 97, pp. 69–82). Springer. https://doi.org/10.1007/978-3-319-03194-1_3 chicago: Muelling, Katharina, Oliver Kroemer, Christoph Lampert, and Bernhard Schölkopf. “Movement Templates for Learning of Hitting and Batting.” In Learning Motor Skills, edited by Jens Kober and Jan Peters, 97:69–82. From Algorithms to Robot Experiments. Springer, 2014. https://doi.org/10.1007/978-3-319-03194-1_3. ieee: K. Muelling, O. Kroemer, C. Lampert, and B. Schölkopf, “Movement templates for learning of hitting and batting,” in Learning Motor Skills, vol. 97, J. Kober and J. Peters, Eds. Springer, 2014, pp. 69–82. ista: 'Muelling K, Kroemer O, Lampert C, Schölkopf B. 2014.Movement templates for learning of hitting and batting. In: Learning Motor Skills. Springer Tracts in Advanced Robotics, vol. 97, 69–82.' mla: Muelling, Katharina, et al. “Movement Templates for Learning of Hitting and Batting.” Learning Motor Skills, edited by Jens Kober and Jan Peters, vol. 97, Springer, 2014, pp. 69–82, doi:10.1007/978-3-319-03194-1_3. short: K. Muelling, O. Kroemer, C. Lampert, B. Schölkopf, in:, J. Kober, J. Peters (Eds.), Learning Motor Skills, Springer, 2014, pp. 69–82. date_created: 2018-12-11T11:54:14Z date_published: 2014-01-01T00:00:00Z date_updated: 2021-01-12T06:53:28Z day: '01' department: - _id: ChLa doi: 10.1007/978-3-319-03194-1_3 editor: - first_name: Jens full_name: Kober, Jens last_name: Kober - first_name: Jan full_name: Peters, Jan last_name: Peters intvolume: ' 97' language: - iso: eng month: '01' oa_version: None page: 69 - 82 publication: Learning Motor Skills publication_status: published publisher: Springer publist_id: '5274' quality_controlled: '1' scopus_import: 1 series_title: From Algorithms to Robot Experiments status: public title: Movement templates for learning of hitting and batting type: book_chapter user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 97 year: '2014' ... --- _id: '2033' abstract: - lang: eng text: 'The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time. Here, we show that privileged information can naturally be treated as noise in the latent function of a Gaussian process classifier (GPC). That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC probit likelihood function. Extensive experiments on public datasets show that the proposed GPC method using privileged noise, called GPC+, improves over a standard GPC without privileged knowledge, and also over the current state-of-the-art SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep learning methods can be compressed as privileged information.' author: - first_name: Daniel full_name: Hernandez Lobato, Daniel last_name: Hernandez Lobato - first_name: Viktoriia full_name: Sharmanska, Viktoriia id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87 last_name: Sharmanska orcid: 0000-0003-0192-9308 - first_name: Kristian full_name: Kersting, Kristian last_name: Kersting - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto citation: ama: 'Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. Mind the nuisance: Gaussian process classification using privileged noise. In: Advances in Neural Information Processing Systems. Vol 1. Neural Information Processing Systems; 2014:837-845.' apa: 'Hernandez Lobato, D., Sharmanska, V., Kersting, K., Lampert, C., & Quadrianto, N. (2014). Mind the nuisance: Gaussian process classification using privileged noise. In Advances in Neural Information Processing Systems (Vol. 1, pp. 837–845). Montreal, Canada: Neural Information Processing Systems.' chicago: 'Hernandez Lobato, Daniel, Viktoriia Sharmanska, Kristian Kersting, Christoph Lampert, and Novi Quadrianto. “Mind the Nuisance: Gaussian Process Classification Using Privileged Noise.” In Advances in Neural Information Processing Systems, 1:837–45. Neural Information Processing Systems, 2014.' ieee: 'D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, and N. Quadrianto, “Mind the nuisance: Gaussian process classification using privileged noise,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2014, vol. 1, no. January, pp. 837–845.' ista: 'Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. 2014. Mind the nuisance: Gaussian process classification using privileged noise. Advances in Neural Information Processing Systems. NIPS: Neural Information Processing Systems vol. 1, 837–845.' mla: 'Hernandez Lobato, Daniel, et al. “Mind the Nuisance: Gaussian Process Classification Using Privileged Noise.” Advances in Neural Information Processing Systems, vol. 1, no. January, Neural Information Processing Systems, 2014, pp. 837–45.' short: D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, N. Quadrianto, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems, 2014, pp. 837–845. conference: end_date: 2014-12-13 location: Montreal, Canada name: 'NIPS: Neural Information Processing Systems' start_date: 2014-12-08 date_created: 2018-12-11T11:55:20Z date_published: 2014-12-08T00:00:00Z date_updated: 2023-02-23T10:25:24Z day: '08' department: - _id: ChLa intvolume: ' 1' issue: January language: - iso: eng main_file_link: - open_access: '1' url: https://papers.nips.cc/paper/5373-mind-the-nuisance-gaussian-process-classification-using-privileged-noise month: '12' oa: 1 oa_version: Submitted Version page: 837-845 publication: Advances in Neural Information Processing Systems publication_status: published publisher: Neural Information Processing Systems publist_id: '5038' quality_controlled: '1' scopus_import: 1 status: public title: 'Mind the nuisance: Gaussian process classification using privileged noise' type: conference user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 1 year: '2014' ... --- _id: '2171' abstract: - lang: eng text: We present LS-CRF, a new method for training cyclic Conditional Random Fields (CRFs) from large datasets that is inspired by classical closed-form expressions for the maximum likelihood parameters of a generative graphical model with tree topology. Training a CRF with LS-CRF requires only solving a set of independent regression problems, each of which can be solved efficiently in closed form or by an iterative solver. This makes LS-CRF orders of magnitude faster than classical CRF training based on probabilistic inference, and at the same time more flexible and easier to implement than other approximate techniques, such as pseudolikelihood or piecewise training. We apply LS-CRF to the task of semantic image segmentation, showing that it achieves on par accuracy to other training techniques at higher speed, thereby allowing efficient CRF training from very large training sets. For example, training a linearly parameterized pairwise CRF on 150,000 images requires less than one hour on a modern workstation. alternative_title: - LNCS author: - first_name: Alexander full_name: Kolesnikov, Alexander id: 2D157DB6-F248-11E8-B48F-1D18A9856A87 last_name: Kolesnikov - first_name: Matthieu full_name: Guillaumin, Matthieu last_name: Guillaumin - first_name: Vittorio full_name: Ferrari, Vittorio last_name: Ferrari - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. Closed-form approximate CRF training for scalable image segmentation. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8691. Springer; 2014:550-565. doi:10.1007/978-3-319-10578-9_36' apa: 'Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form approximate CRF training for scalable image segmentation. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36' chicago: Kolesnikov, Alexander, Matthieu Guillaumin, Vittorio Ferrari, and Christoph Lampert. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, 8691:550–65. Springer, 2014. https://doi.org/10.1007/978-3-319-10578-9_36. ieee: A. Kolesnikov, M. Guillaumin, V. Ferrari, and C. Lampert, “Closed-form approximate CRF training for scalable image segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Zurich, Switzerland, 2014, vol. 8691, no. PART 3, pp. 550–565. ista: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. 2014. Closed-form approximate CRF training for scalable image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ECCV: European Conference on Computer Vision, LNCS, vol. 8691, 550–565.' mla: Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65, doi:10.1007/978-3-319-10578-9_36. short: A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565. conference: end_date: 2014-09-12 location: Zurich, Switzerland name: 'ECCV: European Conference on Computer Vision' start_date: 2014-09-06 date_created: 2018-12-11T11:56:07Z date_published: 2014-09-01T00:00:00Z date_updated: 2021-01-12T06:55:46Z day: '01' department: - _id: ChLa doi: 10.1007/978-3-319-10578-9_36 ec_funded: 1 editor: - first_name: David full_name: Fleet, David last_name: Fleet - first_name: Tomas full_name: Pajdla, Tomas last_name: Pajdla - first_name: Bernt full_name: Schiele, Bernt last_name: Schiele - first_name: Tinne full_name: Tuytelaars, Tinne last_name: Tuytelaars intvolume: ' 8691' issue: PART 3 language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1403.7057 month: '09' oa: 1 oa_version: Submitted Version page: 550 - 565 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) publication_status: published publisher: Springer publist_id: '4813' quality_controlled: '1' scopus_import: 1 status: public title: Closed-form approximate CRF training for scalable image segmentation type: conference user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 8691 year: '2014' ... --- _id: '2173' abstract: - lang: eng text: "In this work we introduce a new approach to co-classification, i.e. the task of jointly classifying multiple, otherwise independent, data samples. The method we present, named CoConut, is based on the idea of adding a regularizer in the label space to encode certain priors on the resulting labelings. A regularizer that encourages labelings that are smooth across the test set, for instance, can be seen as a test-time variant of the cluster assumption, which has been proven useful at training time in semi-supervised learning. A regularizer that introduces a preference for certain class proportions can be regarded as a prior distribution on the class labels. CoConut can build on existing classifiers without making any assumptions on how they were obtained and without the need to re-train them. The use of a regularizer adds a new level of flexibility. It allows the integration of potentially new information at test time, even in other modalities than what the classifiers were trained on. We evaluate our framework on six datasets, reporting a clear performance gain in classification accuracy compared to the standard classification setup that predicts labels for each test sample separately.\r\n" author: - first_name: Sameh full_name: Khamis, Sameh last_name: Khamis - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Khamis S, Lampert C. CoConut: Co-classification with output space regularization. In: Proceedings of the British Machine Vision Conference 2014. BMVA Press; 2014.' apa: 'Khamis, S., & Lampert, C. (2014). CoConut: Co-classification with output space regularization. In Proceedings of the British Machine Vision Conference 2014. Nottingham, UK: BMVA Press.' chicago: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output Space Regularization.” In Proceedings of the British Machine Vision Conference 2014. BMVA Press, 2014.' ieee: 'S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,” in Proceedings of the British Machine Vision Conference 2014, Nottingham, UK, 2014.' ista: 'Khamis S, Lampert C. 2014. CoConut: Co-classification with output space regularization. Proceedings of the British Machine Vision Conference 2014. BMVC: British Machine Vision Conference.' mla: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output Space Regularization.” Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014.' short: S. Khamis, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014. conference: end_date: 2014-09-05 location: Nottingham, UK name: 'BMVC: British Machine Vision Conference' start_date: 2014-09-01 date_created: 2018-12-11T11:56:08Z date_published: 2014-09-01T00:00:00Z date_updated: 2021-01-12T06:55:46Z day: '01' ddc: - '000' department: - _id: ChLa ec_funded: 1 file: - access_level: open_access checksum: c4c6d3efdb8ee648faf3e76849839ce2 content_type: application/pdf creator: system date_created: 2018-12-12T10:08:23Z date_updated: 2020-07-14T12:45:31Z file_id: '4683' file_name: IST-2016-490-v1+1_khamis-bmvc2014.pdf file_size: 408172 relation: main_file file_date_updated: 2020-07-14T12:45:31Z has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: Proceedings of the British Machine Vision Conference 2014 publication_status: published publisher: BMVA Press publist_id: '4811' pubrep_id: '490' quality_controlled: '1' scopus_import: 1 status: public title: 'CoConut: Co-classification with output space regularization' type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2014' ... --- _id: '2172' abstract: - lang: eng text: Fisher Kernels and Deep Learning were two developments with significant impact on large-scale object categorization in the last years. Both approaches were shown to achieve state-of-the-art results on large-scale object categorization datasets, such as ImageNet. Conceptually, however, they are perceived as very different and it is not uncommon for heated debates to spring up when advocates of both paradigms meet at conferences or workshops. In this work, we emphasize the similarities between both architectures rather than their differences and we argue that such a unified view allows us to transfer ideas from one domain to the other. As a concrete example we introduce a method for learning a support vector machine classifier with Fisher kernel at the same time as a task-specific data representation. We reinterpret the setting as a multi-layer feed forward network. Its final layer is the classifier, parameterized by a weight vector, and the two previous layers compute Fisher vectors, parameterized by the coefficients of a Gaussian mixture model. We introduce a gradient descent based learning algorithm that, in contrast to other feature learning techniques, is not just derived from intuition or biological analogy, but has a theoretical justification in the framework of statistical learning theory. Our experiments show that the new training procedure leads to significant improvements in classification accuracy while preserving the modularity and geometric interpretability of a support vector machine setup. author: - first_name: Vladyslav full_name: Sydorov, Vladyslav last_name: Sydorov - first_name: Mayu full_name: Sakurada, Mayu last_name: Sakurada - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Sydorov V, Sakurada M, Lampert C. Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE; 2014:1402-1409. doi:10.1109/CVPR.2014.182' apa: 'Sydorov, V., Sakurada, M., & Lampert, C. (2014). Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1402–1409). Columbus, USA: IEEE. https://doi.org/10.1109/CVPR.2014.182' chicago: Sydorov, Vladyslav, Mayu Sakurada, and Christoph Lampert. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1402–9. IEEE, 2014. https://doi.org/10.1109/CVPR.2014.182. ieee: V. Sydorov, M. Sakurada, and C. Lampert, “Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 1402–1409. ista: 'Sydorov V, Sakurada M, Lampert C. 2014. Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and Pattern Recognition, 1402–1409.' mla: Sydorov, Vladyslav, et al. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–09, doi:10.1109/CVPR.2014.182. short: V. Sydorov, M. Sakurada, C. Lampert, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–1409. conference: end_date: 2014-06-28 location: Columbus, USA name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2014-06-23 date_created: 2018-12-11T11:56:08Z date_published: 2014-09-24T00:00:00Z date_updated: 2021-01-12T06:55:46Z day: '24' department: - _id: ChLa doi: 10.1109/CVPR.2014.182 ec_funded: 1 language: - iso: eng month: '09' oa_version: None page: 1402 - 1409 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition publication_status: published publisher: IEEE publist_id: '4812' quality_controlled: '1' scopus_import: 1 status: public title: Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters type: conference user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 year: '2014' ... --- _id: '2160' abstract: - lang: eng text: Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed. However, relatively little is known about their theoretical properties, especially in the setting of lifelong learning, where the goal is to transfer information to tasks for which no data have been observed so far. In this work we study lifelong learning from a theoretical perspective. Our main result is a PAC-Bayesian generalization bound that offers a unified view on existing paradigms for transfer learning, such as the transfer of parameters or the transfer of low-dimensional representations. We also use the bound to derive two principled lifelong learning algorithms, and we show that these yield results comparable with existing methods. article_processing_charge: No author: - first_name: Anastasia full_name: Pentina, Anastasia id: 42E87FC6-F248-11E8-B48F-1D18A9856A87 last_name: Pentina - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Pentina A, Lampert C. A PAC-Bayesian bound for Lifelong Learning. In: Vol 32. ML Research Press; 2014:991-999.' apa: 'Pentina, A., & Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning (Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine Learning, Beijing, China: ML Research Press.' chicago: Pentina, Anastasia, and Christoph Lampert. “A PAC-Bayesian Bound for Lifelong Learning,” 32:991–99. ML Research Press, 2014. ieee: 'A. Pentina and C. Lampert, “A PAC-Bayesian bound for Lifelong Learning,” presented at the ICML: International Conference on Machine Learning, Beijing, China, 2014, vol. 32, pp. 991–999.' ista: 'Pentina A, Lampert C. 2014. A PAC-Bayesian bound for Lifelong Learning. ICML: International Conference on Machine Learning vol. 32, 991–999.' mla: Pentina, Anastasia, and Christoph Lampert. A PAC-Bayesian Bound for Lifelong Learning. Vol. 32, ML Research Press, 2014, pp. 991–99. short: A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999. conference: end_date: 2014-06-26 location: Beijing, China name: 'ICML: International Conference on Machine Learning' start_date: 2014-06-21 date_created: 2018-12-11T11:56:03Z date_published: 2014-05-10T00:00:00Z date_updated: 2023-10-17T11:54:24Z day: '10' department: - _id: ChLa intvolume: ' 32' language: - iso: eng main_file_link: - open_access: '1' url: https://dl.acm.org/citation.cfm?id=3045003 month: '05' oa: 1 oa_version: Submitted Version page: 991 - 999 publication_status: published publisher: ML Research Press publist_id: '4844' quality_controlled: '1' scopus_import: '1' status: public title: A PAC-Bayesian bound for Lifelong Learning type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 32 year: '2014' ... --- _id: '2294' abstract: - lang: eng text: "In this work we propose a system for automatic classification of Drosophila embryos into developmental stages.\r\nWhile the system is designed to solve an actual problem in biological research, we believe that the principle underly-\r\ning it is interesting not only for biologists, but also for researchers in computer vision. The main idea is to combine two orthogonal sources of information: one is a classifier trained on strongly invariant features, which makes it applicable to images of very different conditions, but also leads to rather noisy predictions. The other is a label propagation step based on a more powerful similarity measure that however is only consistent within specific subsets of the data at a time.\r\nIn our biological setup, the information sources are the shape and the staining patterns of embryo images. We show\r\nexperimentally that while neither of the methods \ can be used by itself to achieve satisfactory results, their combina-\r\ntion achieves prediction quality comparable to human performance." author: - first_name: Tomas full_name: Kazmar, Tomas last_name: Kazmar - first_name: Evgeny full_name: Kvon, Evgeny last_name: Kvon - first_name: Alexander full_name: Stark, Alexander last_name: Stark - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kazmar T, Kvon E, Stark A, Lampert C. Drosophila Embryo Stage Annotation using Label Propagation. In: IEEE; 2013. doi:10.1109/ICCV.2013.139' apa: 'Kazmar, T., Kvon, E., Stark, A., & Lampert, C. (2013). Drosophila Embryo Stage Annotation using Label Propagation. Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.139' chicago: Kazmar, Tomas, Evgeny Kvon, Alexander Stark, and Christoph Lampert. “Drosophila Embryo Stage Annotation Using Label Propagation.” IEEE, 2013. https://doi.org/10.1109/ICCV.2013.139. ieee: 'T. Kazmar, E. Kvon, A. Stark, and C. Lampert, “Drosophila Embryo Stage Annotation using Label Propagation,” presented at the ICCV: International Conference on Computer Vision, Sydney, Australia, 2013.' ista: 'Kazmar T, Kvon E, Stark A, Lampert C. 2013. Drosophila Embryo Stage Annotation using Label Propagation. ICCV: International Conference on Computer Vision.' mla: Kazmar, Tomas, et al. Drosophila Embryo Stage Annotation Using Label Propagation. IEEE, 2013, doi:10.1109/ICCV.2013.139. short: T. Kazmar, E. Kvon, A. Stark, C. Lampert, in:, IEEE, 2013. conference: end_date: 2013-12-08 location: Sydney, Australia name: 'ICCV: International Conference on Computer Vision' start_date: 2013-12-01 date_created: 2018-12-11T11:56:49Z date_published: 2013-12-01T00:00:00Z date_updated: 2021-01-12T06:56:35Z day: '01' department: - _id: ChLa doi: 10.1109/ICCV.2013.139 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: http://www.cv-foundation.org/openaccess/ICCV2013.py month: '12' oa: 1 oa_version: Submitted Version project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_status: published publisher: IEEE publist_id: '4634' quality_controlled: '1' scopus_import: 1 status: public title: Drosophila Embryo Stage Annotation using Label Propagation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2013' ... --- _id: '2293' abstract: - lang: eng text: Many computer vision problems have an asymmetric distribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test time. This situation is called learning using privileged information (LUPI). We introduce two maximum-margin techniques that are able to make use of this additional source of information, and we show that the framework is applicable to several scenarios that have been studied in computer vision before. Experiments with attributes, bounding boxes, image tags and rationales as additional information in object classification show promising results. author: - first_name: Viktoriia full_name: Sharmanska, Viktoriia id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87 last_name: Sharmanska orcid: 0000-0003-0192-9308 - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Sharmanska V, Quadrianto N, Lampert C. Learning to rank using privileged information. In: IEEE; 2013:825-832. doi:10.1109/ICCV.2013.107' apa: 'Sharmanska, V., Quadrianto, N., & Lampert, C. (2013). Learning to rank using privileged information (pp. 825–832). Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.107' chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Learning to Rank Using Privileged Information,” 825–32. IEEE, 2013. https://doi.org/10.1109/ICCV.2013.107. ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Learning to rank using privileged information,” presented at the ICCV: International Conference on Computer Vision, Sydney, Australia, 2013, pp. 825–832.' ista: 'Sharmanska V, Quadrianto N, Lampert C. 2013. Learning to rank using privileged information. ICCV: International Conference on Computer Vision, 825–832.' mla: Sharmanska, Viktoriia, et al. Learning to Rank Using Privileged Information. IEEE, 2013, pp. 825–32, doi:10.1109/ICCV.2013.107. short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832. conference: end_date: 2013-12-08 location: Sydney, Australia name: 'ICCV: International Conference on Computer Vision' start_date: 2013-12-01 date_created: 2018-12-11T11:56:49Z date_published: 2013-12-01T00:00:00Z date_updated: 2023-02-23T10:36:41Z day: '01' department: - _id: ChLa doi: 10.1109/ICCV.2013.107 ec_funded: 1 language: - iso: eng main_file_link: - open_access: '1' url: www.cv-foundation.org/openaccess/content_iccv_2013/papers/Sharmanska_Learning_to_Rank_2013_ICCV_paper.pdf month: '12' oa: 1 oa_version: Submitted Version page: 825 - 832 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_status: published publisher: IEEE publist_id: '4635' quality_controlled: '1' scopus_import: 1 status: public title: Learning to rank using privileged information type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2013' ... --- _id: '2516' abstract: - lang: eng text: 'We study the problem of object recognition for categories for which we have no training examples, a task also called zero-data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently: the world contains tens of thousands of different object classes and for only few of them image collections have been formed and suitably annotated. To tackle the problem we introduce attribute-based classification: objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object''s color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be pre-learned independently, e.g. from existing image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper we also introduce a new dataset, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more datasets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.' author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Hannes full_name: Nickisch, Hannes last_name: Nickisch - first_name: Stefan full_name: Harmeling, Stefan last_name: Harmeling citation: ama: Lampert C, Nickisch H, Harmeling S. Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;36(3):453-465. doi:10.1109/TPAMI.2013.140 apa: Lampert, C., Nickisch, H., & Harmeling, S. (2013). Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2013.140 chicago: Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based Classification for Zero-Shot Learning of Object Categories.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2013. https://doi.org/10.1109/TPAMI.2013.140. ieee: C. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification for zero-shot learning of object categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3. IEEE, pp. 453–465, 2013. ista: Lampert C, Nickisch H, Harmeling S. 2013. Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(3), 453–465. mla: Lampert, Christoph, et al. “Attribute-Based Classification for Zero-Shot Learning of Object Categories.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, IEEE, 2013, pp. 453–65, doi:10.1109/TPAMI.2013.140. short: C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2013) 453–465. date_created: 2018-12-11T11:58:08Z date_published: 2013-07-30T00:00:00Z date_updated: 2021-01-12T06:57:58Z day: '30' department: - _id: ChLa doi: 10.1109/TPAMI.2013.140 intvolume: ' 36' issue: '3' language: - iso: eng month: '07' oa_version: None page: 453 - 465 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_status: published publisher: IEEE publist_id: '4385' quality_controlled: '1' scopus_import: 1 status: public title: Attribute-based classification for zero-shot learning of object categories type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 36 year: '2013' ... --- _id: '2901' abstract: - lang: eng text: ' We introduce the M-modes problem for graphical models: predicting the M label configurations of highest probability that are at the same time local maxima of the probability landscape. M-modes have multiple possible applications: because they are intrinsically diverse, they provide a principled alternative to non-maximum suppression techniques for structured prediction, they can act as codebook vectors for quantizing the configuration space, or they can form component centers for mixture model approximation. We present two algorithms for solving the M-modes problem. The first algorithm solves the problem in polynomial time when the underlying graphical model is a simple chain. The second algorithm solves the problem for junction chains. In synthetic and real dataset, we demonstrate how M-modes can improve the performance of prediction. We also use the generated modes as a tool to understand the topography of the probability distribution of configurations, for example with relation to the training set size and amount of noise in the data. ' alternative_title: - ' JMLR: W&CP' author: - first_name: Chao full_name: Chen, Chao id: 3E92416E-F248-11E8-B48F-1D18A9856A87 last_name: Chen - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Zhu full_name: Yan, Zhu last_name: Yan - first_name: Dimitris full_name: Metaxas, Dimitris last_name: Metaxas - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. Computing the M most probable modes of a graphical model. In: Vol 31. JMLR; 2013:161-169.' apa: 'Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., & Lampert, C. (2013). Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169). Presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States: JMLR.' chicago: Chen, Chao, Vladimir Kolmogorov, Zhu Yan, Dimitris Metaxas, and Christoph Lampert. “Computing the M Most Probable Modes of a Graphical Model,” 31:161–69. JMLR, 2013. ieee: 'C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, and C. Lampert, “Computing the M most probable modes of a graphical model,” presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States, 2013, vol. 31, pp. 161–169.' ista: 'Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. 2013. Computing the M most probable modes of a graphical model. AISTATS: Conference on Uncertainty in Artificial Intelligence, JMLR: W&CP, vol. 31, 161–169.' mla: Chen, Chao, et al. Computing the M Most Probable Modes of a Graphical Model. Vol. 31, JMLR, 2013, pp. 161–69. short: C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, C. Lampert, in:, JMLR, 2013, pp. 161–169. conference: end_date: 2013-05-01 location: Scottsdale, AZ, United States name: ' AISTATS: Conference on Uncertainty in Artificial Intelligence' start_date: 2013-04-29 date_created: 2018-12-11T12:00:14Z date_published: 2013-01-01T00:00:00Z date_updated: 2021-01-12T07:00:35Z day: '01' department: - _id: HeEd - _id: VlKo - _id: ChLa intvolume: ' 31' language: - iso: eng main_file_link: - open_access: '1' url: http://jmlr.org/proceedings/papers/v31/chen13a.html month: '01' oa: 1 oa_version: None page: 161 - 169 publication_status: published publisher: JMLR publist_id: '3846' quality_controlled: '1' scopus_import: 1 status: public title: Computing the M most probable modes of a graphical model type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 31 year: '2013' ... --- _id: '2948' abstract: - lang: eng text: 'Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance.' acknowledgement: This work was supported by the PASCAL 2 Network of Excellence (TT) and by the Newton International Fellowship (NQ) alternative_title: - LNCS author: - first_name: Tatiana full_name: Tommasi, Tatiana last_name: Tommasi - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto - first_name: Barbara full_name: Caputo, Barbara last_name: Caputo - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Tommasi T, Quadrianto N, Caputo B, Lampert C. Beyond dataset bias: Multi-task unaligned shared knowledge transfer. 2013;7724:1-15. doi:10.1007/978-3-642-37331-2_1' apa: 'Tommasi, T., Quadrianto, N., Caputo, B., & Lampert, C. (2013). Beyond dataset bias: Multi-task unaligned shared knowledge transfer. Presented at the ACCV: Asian Conference on Computer Vision, Daejeon, Korea: Springer. https://doi.org/10.1007/978-3-642-37331-2_1' chicago: 'Tommasi, Tatiana, Novi Quadrianto, Barbara Caputo, and Christoph Lampert. “Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer.” Lecture Notes in Computer Science. Springer, 2013. https://doi.org/10.1007/978-3-642-37331-2_1.' ieee: 'T. Tommasi, N. Quadrianto, B. Caputo, and C. Lampert, “Beyond dataset bias: Multi-task unaligned shared knowledge transfer,” vol. 7724. Springer, pp. 1–15, 2013.' ista: 'Tommasi T, Quadrianto N, Caputo B, Lampert C. 2013. Beyond dataset bias: Multi-task unaligned shared knowledge transfer. 7724, 1–15.' mla: 'Tommasi, Tatiana, et al. Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer. Vol. 7724, Springer, 2013, pp. 1–15, doi:10.1007/978-3-642-37331-2_1.' short: T. Tommasi, N. Quadrianto, B. Caputo, C. Lampert, 7724 (2013) 1–15. conference: end_date: 2012-11-09 location: Daejeon, Korea name: 'ACCV: Asian Conference on Computer Vision' start_date: 2012-11-05 date_created: 2018-12-11T12:00:30Z date_published: 2013-04-04T00:00:00Z date_updated: 2020-08-11T10:09:54Z day: '04' ddc: - '000' department: - _id: ChLa doi: 10.1007/978-3-642-37331-2_1 file: - access_level: open_access checksum: a0a7234a89e2192af655b0d0ae3bf445 content_type: application/pdf creator: dernst date_created: 2019-01-22T14:03:11Z date_updated: 2020-07-14T12:45:55Z file_id: '5874' file_name: 2012_ACCV_Tommasi.pdf file_size: 1513620 relation: main_file file_date_updated: 2020-07-14T12:45:55Z has_accepted_license: '1' intvolume: ' 7724' language: - iso: eng month: '04' oa: 1 oa_version: Submitted Version page: 1 - 15 publication_status: published publisher: Springer publist_id: '3784' quality_controlled: '1' scopus_import: 1 series_title: Lecture Notes in Computer Science status: public title: 'Beyond dataset bias: Multi-task unaligned shared knowledge transfer' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 7724 year: '2013' ... --- _id: '3321' author: - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Quadrianto N, Lampert C. Kernel based learning. In: Dubitzky W, Wolkenhauer O, Cho K, Yokota H, eds. Encyclopedia of Systems Biology. Vol 3. Springer; 2013:1069-1069. doi:10.1007/978-1-4419-9863-7_604' apa: Quadrianto, N., & Lampert, C. (2013). Kernel based learning. In W. Dubitzky, O. Wolkenhauer, K. Cho, & H. Yokota (Eds.), Encyclopedia of Systems Biology (Vol. 3, pp. 1069–1069). Springer. https://doi.org/10.1007/978-1-4419-9863-7_604 chicago: Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.” In Encyclopedia of Systems Biology, edited by Werner Dubitzky, Olaf Wolkenhauer, Kwang Cho, and Hiroki Yokota, 3:1069–1069. Springer, 2013. https://doi.org/10.1007/978-1-4419-9863-7_604. ieee: N. Quadrianto and C. Lampert, “Kernel based learning,” in Encyclopedia of Systems Biology, vol. 3, W. Dubitzky, O. Wolkenhauer, K. Cho, and H. Yokota, Eds. Springer, 2013, pp. 1069–1069. ista: 'Quadrianto N, Lampert C. 2013.Kernel based learning. In: Encyclopedia of Systems Biology. vol. 3, 1069–1069.' mla: Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.” Encyclopedia of Systems Biology, edited by Werner Dubitzky et al., vol. 3, Springer, 2013, pp. 1069–1069, doi:10.1007/978-1-4419-9863-7_604. short: N. Quadrianto, C. Lampert, in:, W. Dubitzky, O. Wolkenhauer, K. Cho, H. Yokota (Eds.), Encyclopedia of Systems Biology, Springer, 2013, pp. 1069–1069. date_created: 2018-12-11T12:02:39Z date_published: 2013-01-01T00:00:00Z date_updated: 2021-01-12T07:42:38Z day: '01' department: - _id: ChLa doi: 10.1007/978-1-4419-9863-7_604 editor: - first_name: Werner full_name: Dubitzky, Werner last_name: Dubitzky - first_name: Olaf full_name: Wolkenhauer, Olaf last_name: Wolkenhauer - first_name: Kwang full_name: Cho, Kwang last_name: Cho - first_name: Hiroki full_name: Yokota, Hiroki last_name: Yokota intvolume: ' 3' language: - iso: eng month: '01' oa_version: None page: 1069 - 1069 publication: Encyclopedia of Systems Biology publication_status: published publisher: Springer publist_id: '3314' quality_controlled: '1' status: public title: Kernel based learning type: encyclopedia_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 3 year: '2013' ... --- _id: '2825' abstract: - lang: eng text: 'We study the problem of maximum marginal prediction (MMP) in probabilistic graphical models, a task that occurs, for example, as the Bayes optimal decision rule under a Hamming loss. MMP is typically performed as a two-stage procedure: one estimates each variable''s marginal probability and then forms a prediction from the states of maximal probability. In this work we propose a simple yet effective technique for accelerating MMP when inference is sampling-based: instead of the above two-stage procedure we directly estimate the posterior probability of each decision variable. This allows us to identify the point of time when we are sufficiently certain about any individual decision. Whenever this is the case, we dynamically prune the variables we are confident about from the underlying factor graph. Consequently, at any time only samples of variables whose decision is still uncertain need to be created. Experiments in two prototypical scenarios, multi-label classification and image inpainting, show that adaptive sampling can drastically accelerate MMP without sacrificing prediction accuracy.' author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Lampert C. Dynamic pruning of factor graphs for maximum marginal prediction. In: Vol 1. Neural Information Processing Systems; 2012:82-90.' apa: 'Lampert, C. (2012). Dynamic pruning of factor graphs for maximum marginal prediction (Vol. 1, pp. 82–90). Presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV, United States: Neural Information Processing Systems.' chicago: Lampert, Christoph. “Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction,” 1:82–90. Neural Information Processing Systems, 2012. ieee: 'C. Lampert, “Dynamic pruning of factor graphs for maximum marginal prediction,” presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV, United States, 2012, vol. 1, pp. 82–90.' ista: 'Lampert C. 2012. Dynamic pruning of factor graphs for maximum marginal prediction. NIPS: Neural Information Processing Systems vol. 1, 82–90.' mla: Lampert, Christoph. Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction. Vol. 1, Neural Information Processing Systems, 2012, pp. 82–90. short: C. Lampert, in:, Neural Information Processing Systems, 2012, pp. 82–90. conference: end_date: 2012-12-06 location: Lake Tahoe, NV, United States name: 'NIPS: Neural Information Processing Systems' start_date: 2012-12-03 date_created: 2018-12-11T11:59:48Z date_published: 2012-12-01T00:00:00Z date_updated: 2021-01-12T06:59:59Z day: '01' department: - _id: ChLa intvolume: ' 1' language: - iso: eng month: '12' oa_version: None page: 82 - 90 publication_status: published publisher: Neural Information Processing Systems publist_id: '3975' quality_controlled: '1' scopus_import: 1 status: public title: Dynamic pruning of factor graphs for maximum marginal prediction type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 1 year: '2012' ... --- _id: '3164' abstract: - lang: eng text: Overview of the Special Issue on structured prediction and inference. author: - first_name: Matthew full_name: Blaschko, Matthew last_name: Blaschko - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Blaschko M, Lampert C. Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision. 2012;99(3):257-258. doi:10.1007/s11263-012-0530-y' apa: 'Blaschko, M., & Lampert, C. (2012). Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision. Springer. https://doi.org/10.1007/s11263-012-0530-y' chicago: 'Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue on Structured Prediction and Inference.” International Journal of Computer Vision. Springer, 2012. https://doi.org/10.1007/s11263-012-0530-y.' ieee: 'M. Blaschko and C. Lampert, “Guest editorial: Special issue on structured prediction and inference,” International Journal of Computer Vision, vol. 99, no. 3. Springer, pp. 257–258, 2012.' ista: 'Blaschko M, Lampert C. 2012. Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision. 99(3), 257–258.' mla: 'Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue on Structured Prediction and Inference.” International Journal of Computer Vision, vol. 99, no. 3, Springer, 2012, pp. 257–58, doi:10.1007/s11263-012-0530-y.' short: M. Blaschko, C. Lampert, International Journal of Computer Vision 99 (2012) 257–258. date_created: 2018-12-11T12:01:46Z date_published: 2012-09-01T00:00:00Z date_updated: 2021-01-12T07:41:30Z day: '01' department: - _id: ChLa doi: 10.1007/s11263-012-0530-y intvolume: ' 99' issue: '3' language: - iso: eng month: '09' oa_version: None page: 257 - 258 publication: International Journal of Computer Vision publication_status: published publisher: Springer publist_id: '3521' quality_controlled: '1' scopus_import: 1 status: public title: 'Guest editorial: Special issue on structured prediction and inference' type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 99 year: '2012' ... --- _id: '3125' abstract: - lang: eng text: We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone. alternative_title: - LNCS article_processing_charge: No author: - first_name: Viktoriia full_name: Sharmanska, Viktoriia id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87 last_name: Sharmanska orcid: 0000-0003-0192-9308 - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Sharmanska V, Quadrianto N, Lampert C. Augmented attribute representations. In: Vol 7576. Springer; 2012:242-255. doi:10.1007/978-3-642-33715-4_18' apa: 'Sharmanska, V., Quadrianto, N., & Lampert, C. (2012). Augmented attribute representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference on Computer Vision, Florence, Italy: Springer. https://doi.org/10.1007/978-3-642-33715-4_18' chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Augmented Attribute Representations,” 7576:242–55. Springer, 2012. https://doi.org/10.1007/978-3-642-33715-4_18. ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Augmented attribute representations,” presented at the ECCV: European Conference on Computer Vision, Florence, Italy, 2012, vol. 7576, no. PART 5, pp. 242–255.' ista: 'Sharmanska V, Quadrianto N, Lampert C. 2012. Augmented attribute representations. ECCV: European Conference on Computer Vision, LNCS, vol. 7576, 242–255.' mla: Sharmanska, Viktoriia, et al. Augmented Attribute Representations. Vol. 7576, no. PART 5, Springer, 2012, pp. 242–55, doi:10.1007/978-3-642-33715-4_18. short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, Springer, 2012, pp. 242–255. conference: end_date: 2012-10-13 location: Florence, Italy name: 'ECCV: European Conference on Computer Vision' start_date: 2012-10-07 date_created: 2018-12-11T12:01:32Z date_published: 2012-10-01T00:00:00Z date_updated: 2023-02-23T11:13:25Z day: '01' ddc: - '000' department: - _id: ChLa doi: 10.1007/978-3-642-33715-4_18 file: - access_level: open_access checksum: bccdbe0663780d25a1e0524002b2d896 content_type: application/pdf creator: dernst date_created: 2020-05-15T12:29:04Z date_updated: 2020-07-14T12:46:00Z file_id: '7861' file_name: 2012_ECCV_Sharmanska.pdf file_size: 6073897 relation: main_file file_date_updated: 2020-07-14T12:46:00Z has_accepted_license: '1' intvolume: ' 7576' issue: PART 5 language: - iso: eng month: '10' oa: 1 oa_version: Submitted Version page: 242 - 255 publication_status: published publisher: Springer publist_id: '3574' quality_controlled: '1' scopus_import: 1 status: public title: Augmented attribute representations type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 7576 year: '2012' ... --- _id: '3126' abstract: - lang: eng text: "In this work we propose a new information-theoretic clustering algorithm that infers cluster memberships by direct optimization of a non-parametric mutual information estimate between data distribution and cluster assignment. Although the optimization objective has a solid theoretical foundation it is hard to optimize. We propose an approximate optimization formulation that leads to an efficient algorithm with low runtime complexity. The algorithm has a single free parameter, the number of clusters to find. We demonstrate superior performance on several synthetic and real datasets.\r\n" alternative_title: - LNCS author: - first_name: Andreas full_name: Müller, Andreas last_name: Müller - first_name: Sebastian full_name: Nowozin, Sebastian last_name: Nowozin - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Müller A, Nowozin S, Lampert C. Information theoretic clustering using minimal spanning trees. In: Vol 7476. Springer; 2012:205-215. doi:10.1007/978-3-642-32717-9_21' apa: 'Müller, A., Nowozin, S., & Lampert, C. (2012). Information theoretic clustering using minimal spanning trees (Vol. 7476, pp. 205–215). Presented at the DAGM: German Association For Pattern Recognition, Graz, Austria: Springer. https://doi.org/10.1007/978-3-642-32717-9_21' chicago: Müller, Andreas, Sebastian Nowozin, and Christoph Lampert. “Information Theoretic Clustering Using Minimal Spanning Trees,” 7476:205–15. Springer, 2012. https://doi.org/10.1007/978-3-642-32717-9_21. ieee: 'A. Müller, S. Nowozin, and C. Lampert, “Information theoretic clustering using minimal spanning trees,” presented at the DAGM: German Association For Pattern Recognition, Graz, Austria, 2012, vol. 7476, pp. 205–215.' ista: 'Müller A, Nowozin S, Lampert C. 2012. Information theoretic clustering using minimal spanning trees. DAGM: German Association For Pattern Recognition, LNCS, vol. 7476, 205–215.' mla: Müller, Andreas, et al. Information Theoretic Clustering Using Minimal Spanning Trees. Vol. 7476, Springer, 2012, pp. 205–15, doi:10.1007/978-3-642-32717-9_21. short: A. Müller, S. Nowozin, C. Lampert, in:, Springer, 2012, pp. 205–215. conference: end_date: 2012-08-31 location: Graz, Austria name: 'DAGM: German Association For Pattern Recognition' start_date: 2012-08-28 date_created: 2018-12-11T12:01:32Z date_published: 2012-08-14T00:00:00Z date_updated: 2021-01-12T07:41:14Z day: '14' department: - _id: ChLa doi: 10.1007/978-3-642-32717-9_21 intvolume: ' 7476' language: - iso: eng month: '08' oa_version: None page: 205 - 215 publication_status: published publisher: Springer publist_id: '3573' quality_controlled: '1' scopus_import: 1 status: public title: Information theoretic clustering using minimal spanning trees type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 7476 year: '2012' ... --- _id: '3248' abstract: - lang: eng text: We describe RTblob, a high speed vision system that detects objects in cluttered scenes based on their color and shape at a speed of over 800 frames/s. Because the system is available as open-source software and relies only on off-the-shelf PC hardware components, it can provide the basis for multiple application scenarios. As an illustrative example, we show how RTblob can be used in a robotic table tennis scenario to estimate ball trajectories through 3D space simultaneously from four cameras images at a speed of 200 Hz. article_processing_charge: No article_type: original author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Jan full_name: Peters, Jan last_name: Peters citation: ama: Lampert C, Peters J. Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing. 2012;7(1):31-41. doi:10.1007/s11554-010-0168-3 apa: Lampert, C., & Peters, J. (2012). Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing. Springer. https://doi.org/10.1007/s11554-010-0168-3 chicago: Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects in Multiple Camera Streams with off-the-Shelf Hardware Components.” Journal of Real-Time Image Processing. Springer, 2012. https://doi.org/10.1007/s11554-010-0168-3. ieee: C. Lampert and J. Peters, “Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components,” Journal of Real-Time Image Processing, vol. 7, no. 1. Springer, pp. 31–41, 2012. ista: Lampert C, Peters J. 2012. Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing. 7(1), 31–41. mla: Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects in Multiple Camera Streams with off-the-Shelf Hardware Components.” Journal of Real-Time Image Processing, vol. 7, no. 1, Springer, 2012, pp. 31–41, doi:10.1007/s11554-010-0168-3. short: C. Lampert, J. Peters, Journal of Real-Time Image Processing 7 (2012) 31–41. date_created: 2018-12-11T12:02:15Z date_published: 2012-03-01T00:00:00Z date_updated: 2022-05-24T08:05:40Z day: '01' ddc: - '000' department: - _id: ChLa doi: 10.1007/s11554-010-0168-3 file: - access_level: open_access checksum: 241be47ea50e81a283bcf4c45b07e8cc content_type: application/pdf creator: kschuh date_created: 2019-02-12T10:52:25Z date_updated: 2020-07-14T12:46:04Z file_id: '5958' file_name: 2012_Springer_Lampert.pdf file_size: 2933187 relation: main_file file_date_updated: 2020-07-14T12:46:04Z has_accepted_license: '1' intvolume: ' 7' issue: '1' language: - iso: eng month: '03' oa: 1 oa_version: Submitted Version page: 31 - 41 publication: Journal of Real-Time Image Processing publication_identifier: eissn: - 1861-8219 issn: - 1861-8200 publication_status: published publisher: Springer publist_id: '3417' quality_controlled: '1' scopus_import: '1' status: public title: Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 7 year: '2012' ... --- _id: '3124' abstract: - lang: eng text: "We consider the problem of inference in a graphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pairwise terms over a discretized domain. This allows the use of techniques originally developed for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it from training data. Experimental results show that for certain types of graphs a learned function can outperform the Bethe approximation. We also establish a link between the Bethe free energy and submodular functions.\r\n" alternative_title: - Inferning 2012 author: - first_name: Filip full_name: Korc, Filip id: 476A2FD6-F248-11E8-B48F-1D18A9856A87 last_name: Korc - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Korc F, Kolmogorov V, Lampert C. Approximating marginals using discrete energy minimization. In: ICML; 2012.' apa: 'Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals using discrete energy minimization. Presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland: ICML.' chicago: Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. “Approximating Marginals Using Discrete Energy Minimization.” ICML, 2012. ieee: 'F. Korc, V. Kolmogorov, and C. Lampert, “Approximating marginals using discrete energy minimization,” presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland, 2012.' ista: 'Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete energy minimization. ICML: International Conference on Machine Learning, Inferning 2012, .' mla: Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization. ICML, 2012. short: F. Korc, V. Kolmogorov, C. Lampert, in:, ICML, 2012. conference: end_date: 2012-07-01 location: Edinburgh, Scotland name: 'ICML: International Conference on Machine Learning' start_date: 2012-06-26 date_created: 2018-12-11T12:01:31Z date_published: 2012-06-30T00:00:00Z date_updated: 2023-02-23T12:24:24Z day: '30' ddc: - '000' department: - _id: ChLa - _id: VlKo file: - access_level: open_access checksum: 3d0d4246548c736857302aadb2ff5d15 content_type: application/pdf creator: system date_created: 2018-12-12T10:11:34Z date_updated: 2020-07-14T12:46:00Z file_id: '4889' file_name: IST-2016-565-v1+1_DM-inferning2012.pdf file_size: 305836 relation: main_file file_date_updated: 2020-07-14T12:46:00Z has_accepted_license: '1' language: - iso: eng month: '06' oa: 1 oa_version: Submitted Version publication_status: published publisher: ICML publist_id: '3575' pubrep_id: '565' quality_controlled: '1' related_material: record: - id: '5396' relation: later_version status: public status: public title: Approximating marginals using discrete energy minimization type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2012' ... --- _id: '5396' abstract: - lang: eng text: We consider the problem of inference in agraphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pair-wise terms over a discretized domain. This allows the use of techniques originally devel-oped for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it from training data. Experimental results show that for certain types of graphs a learned function can out-perform the Bethe approximation. We also establish a link between the Bethe free energy and submodular functions. alternative_title: - IST Austria Technical Report author: - first_name: Filip full_name: Korc, Filip id: 476A2FD6-F248-11E8-B48F-1D18A9856A87 last_name: Korc - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Korc F, Kolmogorov V, Lampert C. Approximating Marginals Using Discrete Energy Minimization. IST Austria; 2012. doi:10.15479/AT:IST-2012-0003 apa: Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals using discrete energy minimization. IST Austria. https://doi.org/10.15479/AT:IST-2012-0003 chicago: Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. Approximating Marginals Using Discrete Energy Minimization. IST Austria, 2012. https://doi.org/10.15479/AT:IST-2012-0003. ieee: F. Korc, V. Kolmogorov, and C. Lampert, Approximating marginals using discrete energy minimization. IST Austria, 2012. ista: Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete energy minimization, IST Austria, 13p. mla: Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization. IST Austria, 2012, doi:10.15479/AT:IST-2012-0003. short: F. Korc, V. Kolmogorov, C. Lampert, Approximating Marginals Using Discrete Energy Minimization, IST Austria, 2012. date_created: 2018-12-12T11:39:06Z date_published: 2012-07-23T00:00:00Z date_updated: 2023-02-23T11:13:22Z day: '23' ddc: - '000' department: - _id: VlKo - _id: ChLa doi: 10.15479/AT:IST-2012-0003 file: - access_level: open_access checksum: 7e0ba85ad123b13223aaf6cdde2d288c content_type: application/pdf creator: system date_created: 2018-12-12T11:53:29Z date_updated: 2020-07-14T12:46:44Z file_id: '5490' file_name: IST-2012-0003_IST-2012-0003.pdf file_size: 618744 relation: main_file file_date_updated: 2020-07-14T12:46:44Z has_accepted_license: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '13' publication_identifier: issn: - 2664-1690 publication_status: published publisher: IST Austria pubrep_id: '36' related_material: record: - id: '3124' relation: earlier_version status: public status: public title: Approximating marginals using discrete energy minimization type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2012' ... --- _id: '2915' acknowledgement: "The project receives funding from the European Community’s Seventh Framework Programme under grant agreement\r\nno. ICT- 248273 GeRT." article_processing_charge: No author: - first_name: Oliver full_name: Kroemer, Oliver last_name: Kroemer - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Jan full_name: Peters, Jan last_name: Peters citation: ama: 'Kroemer O, Lampert C, Peters J. Multi-modal learning for dynamic tactile sensing. In: Deutsches Zentrum für Luft und Raumfahrt; 2012.' apa: Kroemer, O., Lampert, C., & Peters, J. (2012). Multi-modal learning for dynamic tactile sensing. Deutsches Zentrum für Luft und Raumfahrt. chicago: Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Multi-Modal Learning for Dynamic Tactile Sensing.” Deutsches Zentrum für Luft und Raumfahrt, 2012. ieee: O. Kroemer, C. Lampert, and J. Peters, “Multi-modal learning for dynamic tactile sensing,” 2012. ista: Kroemer O, Lampert C, Peters J. 2012. Multi-modal learning for dynamic tactile sensing mla: Kroemer, Oliver, et al. Multi-Modal Learning for Dynamic Tactile Sensing. Deutsches Zentrum für Luft und Raumfahrt, 2012. short: O. Kroemer, C. Lampert, J. Peters, in:, Deutsches Zentrum für Luft und Raumfahrt, 2012. date_created: 2018-12-11T12:00:19Z date_published: 2012-10-11T00:00:00Z date_updated: 2023-10-17T07:58:59Z day: '11' department: - _id: ChLa language: - iso: eng month: '10' oa_version: None publication_status: published publisher: Deutsches Zentrum für Luft und Raumfahrt publist_id: '3828' quality_controlled: '1' status: public title: Multi-modal learning for dynamic tactile sensing type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2012' ... --- _id: '3127' abstract: - lang: eng text: "When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques.\r\n We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data, we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations." article_processing_charge: No author: - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Chao full_name: Chen, Chao id: 3E92416E-F248-11E8-B48F-1D18A9856A87 last_name: Chen citation: ama: 'Quadrianto N, Lampert C, Chen C. The most persistent soft-clique in a set of sampled graphs. In: Proceedings of the 29th International Conference on Machine Learning. ML Research Press; 2012:211-218.' apa: 'Quadrianto, N., Lampert, C., & Chen, C. (2012). The most persistent soft-clique in a set of sampled graphs. In Proceedings of the 29th International Conference on Machine Learning (pp. 211–218). Edinburgh, United Kingdom: ML Research Press.' chicago: Quadrianto, Novi, Christoph Lampert, and Chao Chen. “The Most Persistent Soft-Clique in a Set of Sampled Graphs.” In Proceedings of the 29th International Conference on Machine Learning, 211–18. ML Research Press, 2012. ieee: N. Quadrianto, C. Lampert, and C. Chen, “The most persistent soft-clique in a set of sampled graphs,” in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, United Kingdom, 2012, pp. 211–218. ista: 'Quadrianto N, Lampert C, Chen C. 2012. The most persistent soft-clique in a set of sampled graphs. Proceedings of the 29th International Conference on Machine Learning. ICML: International Conference on Machine Learning, 211–218.' mla: Quadrianto, Novi, et al. “The Most Persistent Soft-Clique in a Set of Sampled Graphs.” Proceedings of the 29th International Conference on Machine Learning, ML Research Press, 2012, pp. 211–18. short: N. Quadrianto, C. Lampert, C. Chen, in:, Proceedings of the 29th International Conference on Machine Learning, ML Research Press, 2012, pp. 211–218. conference: end_date: 2012-07-01 location: Edinburgh, United Kingdom name: 'ICML: International Conference on Machine Learning' start_date: 2012-06-26 date_created: 2018-12-11T12:01:33Z date_published: 2012-06-01T00:00:00Z date_updated: 2023-10-17T11:55:06Z day: '01' department: - _id: ChLa - _id: HeEd language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1206.4652 month: '06' oa: 1 oa_version: Preprint page: 211-218 publication: Proceedings of the 29th International Conference on Machine Learning publication_status: published publisher: ML Research Press publist_id: '3572' quality_controlled: '1' scopus_import: '1' status: public title: The most persistent soft-clique in a set of sampled graphs type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2012' ... --- _id: '3337' abstract: - lang: eng text: Playing table tennis is a difficult task for robots, especially due to their limitations of acceleration. A key bottleneck is the amount of time needed to reach the desired hitting position and velocity of the racket for returning the incoming ball. Here, it often does not suffice to simply extrapolate the ball's trajectory after the opponent returns it but more information is needed. Humans are able to predict the ball's trajectory based on the opponent's moves and, thus, have a considerable advantage. Hence, we propose to incorporate an anticipation system into robot table tennis players, which enables the robot to react earlier while the opponent is performing the striking movement. Based on visual observation of the opponent's racket movement, the robot can predict the aim of the opponent and adjust its movement generation accordingly. The policies for deciding how and when to react are obtained by reinforcement learning. We conduct experiments with an existing robot player to show that the learned reaction policy can significantly improve the performance of the overall system. author: - first_name: Zhikun full_name: Wang, Zhikun last_name: Wang - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Katharina full_name: Mülling, Katharina last_name: Mülling - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Jan full_name: Peters, Jan last_name: Peters citation: ama: 'Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. Learning anticipation policies for robot table tennis. In: IEEE; 2011:332-337. doi:10.1109/IROS.2011.6094892' apa: 'Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., & Peters, J. (2011). Learning anticipation policies for robot table tennis (pp. 332–337). Presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA: IEEE. https://doi.org/10.1109/IROS.2011.6094892' chicago: Wang, Zhikun, Christoph Lampert, Katharina Mülling, Bernhard Schölkopf, and Jan Peters. “Learning Anticipation Policies for Robot Table Tennis,” 332–37. IEEE, 2011. https://doi.org/10.1109/IROS.2011.6094892. ieee: 'Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, and J. Peters, “Learning anticipation policies for robot table tennis,” presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA, 2011, pp. 332–337.' ista: 'Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. 2011. Learning anticipation policies for robot table tennis. IROS: RSJ International Conference on Intelligent Robots and Systems, 332–337.' mla: Wang, Zhikun, et al. Learning Anticipation Policies for Robot Table Tennis. IEEE, 2011, pp. 332–37, doi:10.1109/IROS.2011.6094892. short: Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, J. Peters, in:, IEEE, 2011, pp. 332–337. conference: end_date: 2011-09-30 location: San Francisco, USA name: 'IROS: RSJ International Conference on Intelligent Robots and Systems' start_date: 2011-09-25 date_created: 2018-12-11T12:02:45Z date_published: 2011-01-01T00:00:00Z date_updated: 2021-01-12T07:42:45Z day: '01' department: - _id: ChLa doi: 10.1109/IROS.2011.6094892 language: - iso: eng month: '01' oa_version: None page: 332 - 337 publication_status: published publisher: IEEE publist_id: '3293' quality_controlled: '1' scopus_import: 1 status: public title: Learning anticipation policies for robot table tennis type: conference user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 year: '2011' ... --- _id: '3389' abstract: - lang: eng text: Kernel canonical correlation analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying process that generates the data and can ignore high-variance noise directions. However, for data where acquisition in one or more modalities is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multi-variate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of KCCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing. acknowledgement: The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 228180. This work was funded in part by the EC project CLASS, IST 027978, and the PASCAL2 network of excellence, IST 2002-506778. author: - first_name: Matthew full_name: Blaschko, Matthew last_name: Blaschko - first_name: Jacquelyn full_name: Shelton, Jacquelyn last_name: Shelton - first_name: Andreas full_name: Bartels, Andreas last_name: Bartels - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Arthur full_name: Gretton, Arthur last_name: Gretton citation: ama: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters. 2011;32(11):1572-1583. doi:10.1016/j.patrec.2011.02.011 apa: Blaschko, M., Shelton, J., Bartels, A., Lampert, C., & Gretton, A. (2011). Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters. Elsevier. https://doi.org/10.1016/j.patrec.2011.02.011 chicago: Blaschko, Matthew, Jacquelyn Shelton, Andreas Bartels, Christoph Lampert, and Arthur Gretton. “Semi Supervised Kernel Canonical Correlation Analysis with Application to Human FMRI.” Pattern Recognition Letters. Elsevier, 2011. https://doi.org/10.1016/j.patrec.2011.02.011. ieee: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, and A. Gretton, “Semi supervised kernel canonical correlation analysis with application to human fMRI,” Pattern Recognition Letters, vol. 32, no. 11. Elsevier, pp. 1572–1583, 2011. ista: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. 2011. Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters. 32(11), 1572–1583. mla: Blaschko, Matthew, et al. “Semi Supervised Kernel Canonical Correlation Analysis with Application to Human FMRI.” Pattern Recognition Letters, vol. 32, no. 11, Elsevier, 2011, pp. 1572–83, doi:10.1016/j.patrec.2011.02.011. short: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition Letters 32 (2011) 1572–1583. date_created: 2018-12-11T12:03:03Z date_published: 2011-08-01T00:00:00Z date_updated: 2021-01-12T07:43:09Z day: '01' department: - _id: ChLa doi: 10.1016/j.patrec.2011.02.011 intvolume: ' 32' issue: '11' language: - iso: eng month: '08' oa_version: None page: 1572 - 1583 publication: Pattern Recognition Letters publication_status: published publisher: Elsevier publist_id: '3218' quality_controlled: '1' scopus_import: 1 status: public title: Semi supervised kernel canonical correlation analysis with application to human fMRI type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 32 year: '2011' ... --- _id: '3382' abstract: - lang: eng text: Dynamic tactile sensing is a fundamental ability to recognize materials and objects. However, while humans are born with partially developed dynamic tactile sensing and quickly master this skill, today's robots remain in their infancy. The development of such a sense requires not only better sensors but the right algorithms to deal with these sensors' data as well. For example, when classifying a material based on touch, the data are noisy, high-dimensional, and contain irrelevant signals as well as essential ones. Few classification methods from machine learning can deal with such problems. In this paper, we propose an efficient approach to infer suitable lower dimensional representations of the tactile data. In order to classify materials based on only the sense of touch, these representations are autonomously discovered using visual information of the surfaces during training. However, accurately pairing vision and tactile samples in real-robot applications is a difficult problem. The proposed approach, therefore, works with weak pairings between the modalities. Experiments show that the resulting approach is very robust and yields significantly higher classification performance based on only dynamic tactile sensing. author: - first_name: Oliver full_name: Kroemer, Oliver last_name: Kroemer - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Jan full_name: Peters, Jan last_name: Peters citation: ama: Kroemer O, Lampert C, Peters J. Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. 2011;27(3):545-557. doi:10.1109/TRO.2011.2121130 apa: Kroemer, O., Lampert, C., & Peters, J. (2011). Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. IEEE. https://doi.org/10.1109/TRO.2011.2121130 chicago: Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Learning Dynamic Tactile Sensing with Robust Vision Based Training.” IEEE Transactions on Robotics. IEEE, 2011. https://doi.org/10.1109/TRO.2011.2121130. ieee: O. Kroemer, C. Lampert, and J. Peters, “Learning dynamic tactile sensing with robust vision based training,” IEEE Transactions on Robotics, vol. 27, no. 3. IEEE, pp. 545–557, 2011. ista: Kroemer O, Lampert C, Peters J. 2011. Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. 27(3), 545–557. mla: Kroemer, Oliver, et al. “Learning Dynamic Tactile Sensing with Robust Vision Based Training.” IEEE Transactions on Robotics, vol. 27, no. 3, IEEE, 2011, pp. 545–57, doi:10.1109/TRO.2011.2121130. short: O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011) 545–557. date_created: 2018-12-11T12:03:01Z date_published: 2011-05-21T00:00:00Z date_updated: 2021-01-12T07:43:06Z day: '21' department: - _id: ChLa doi: 10.1109/TRO.2011.2121130 intvolume: ' 27' issue: '3' language: - iso: eng month: '05' oa_version: None page: 545 - 557 publication: IEEE Transactions on Robotics publication_status: published publisher: IEEE publist_id: '3225' quality_controlled: '1' scopus_import: 1 status: public title: Learning dynamic tactile sensing with robust vision based training type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 27 year: '2011' ... --- _id: '5386' abstract: - lang: eng text: 'We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks.' alternative_title: - IST Austria Technical Report author: - first_name: Chao full_name: Chen, Chao id: 3E92416E-F248-11E8-B48F-1D18A9856A87 last_name: Chen - first_name: Daniel full_name: Freedman, Daniel last_name: Freedman - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Chen C, Freedman D, Lampert C. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria; 2011. doi:10.15479/AT:IST-2011-0002 apa: Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. IST Austria. https://doi.org/10.15479/AT:IST-2011-0002 chicago: Chen, Chao, Daniel Freedman, and Christoph Lampert. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria, 2011. https://doi.org/10.15479/AT:IST-2011-0002. ieee: C. Chen, D. Freedman, and C. Lampert, Enforcing topological constraints in random field image segmentation. IST Austria, 2011. ista: Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in random field image segmentation, IST Austria, 69p. mla: Chen, Chao, et al. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria, 2011, doi:10.15479/AT:IST-2011-0002. short: C. Chen, D. Freedman, C. Lampert, Enforcing Topological Constraints in Random Field Image Segmentation, IST Austria, 2011. date_created: 2018-12-12T11:39:02Z date_published: 2011-03-28T00:00:00Z date_updated: 2023-02-23T11:22:48Z day: '28' ddc: - '000' department: - _id: ChLa doi: 10.15479/AT:IST-2011-0002 file: - access_level: open_access checksum: ad64c2add5fe2ad10e9d5c669f3f9526 content_type: application/pdf creator: system date_created: 2018-12-12T11:53:34Z date_updated: 2020-07-14T12:46:41Z file_id: '5495' file_name: IST-2011-0002_IST-2011-0002.pdf file_size: 26390601 relation: main_file file_date_updated: 2020-07-14T12:46:41Z has_accepted_license: '1' language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: '69' publication_identifier: issn: - 2664-1690 publication_status: published publisher: IST Austria pubrep_id: '22' related_material: record: - id: '3336' relation: later_version status: public status: public title: Enforcing topological constraints in random field image segmentation type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2011' ... --- _id: '3336' abstract: - lang: eng text: 'We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks.' acknowledgement: The first author is supported by the Austrian Science Fund (FWF) grant No. P20134-N13. The authors would like to thank Sebastian Nowozin for helpful discussions. article_processing_charge: No author: - first_name: Chao full_name: Chen, Chao id: 3E92416E-F248-11E8-B48F-1D18A9856A87 last_name: Chen - first_name: Daniel full_name: Freedman, Daniel last_name: Freedman - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Chen C, Freedman D, Lampert C. Enforcing topological constraints in random field image segmentation. In: CVPR: Computer Vision and Pattern Recognition. IEEE; 2011:2089-2096. doi:10.1109/CVPR.2011.5995503' apa: 'Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. In CVPR: Computer Vision and Pattern Recognition (pp. 2089–2096). Colorado Springs, CO, United States: IEEE. https://doi.org/10.1109/CVPR.2011.5995503' chicago: 'Chen, Chao, Daniel Freedman, and Christoph Lampert. “Enforcing Topological Constraints in Random Field Image Segmentation.” In CVPR: Computer Vision and Pattern Recognition, 2089–96. IEEE, 2011. https://doi.org/10.1109/CVPR.2011.5995503.' ieee: 'C. Chen, D. Freedman, and C. Lampert, “Enforcing topological constraints in random field image segmentation,” in CVPR: Computer Vision and Pattern Recognition, Colorado Springs, CO, United States, 2011, pp. 2089–2096.' ista: 'Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in random field image segmentation. CVPR: Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 2089–2096.' mla: 'Chen, Chao, et al. “Enforcing Topological Constraints in Random Field Image Segmentation.” CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–96, doi:10.1109/CVPR.2011.5995503.' short: 'C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–2096.' conference: end_date: 2011-06-25 location: Colorado Springs, CO, United States name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2011-06-20 date_created: 2018-12-11T12:02:45Z date_published: 2011-07-22T00:00:00Z date_updated: 2023-02-23T12:23:56Z day: '22' department: - _id: HeEd - _id: ChLa doi: 10.1109/CVPR.2011.5995503 language: - iso: eng month: '07' oa_version: None page: 2089 - 2096 publication: 'CVPR: Computer Vision and Pattern Recognition' publication_identifier: eisbn: - 978-1-4577-0395-9 isbn: - 978-1-4577-0394-2 publication_status: published publisher: IEEE publist_id: '3294' quality_controlled: '1' related_material: record: - id: '5386' relation: earlier_version status: public scopus_import: '1' status: public title: Enforcing topological constraints in random field image segmentation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2011' ... --- _id: '3163' abstract: - lang: eng text: We study multi-label prediction for structured output sets, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multilabel classification techniques are typically not applicable in this situation, because they require explicit enumeration of the label set, which is infeasible in case of structured outputs. Relying on techniques originally designed for single-label structured prediction, in particular structured support vector machines, results in reduced prediction accuracy, or leads to infeasible optimization problems. In this work we derive a maximum-margin training formulation for multi-label structured prediction that remains computationally tractable while achieving high prediction accuracy. It also shares most beneficial properties with single-label maximum-margin approaches, in particular formulation as a convex optimization problem, efficient working set training, and PAC-Bayesian generalization bounds. author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Lampert C. Maximum margin multi-label structured prediction. In: Neural Information Processing Systems; 2011.' apa: 'Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information Processing Systems.' chicago: Lampert, Christoph. “Maximum Margin Multi-Label Structured Prediction.” Neural Information Processing Systems, 2011. ieee: 'C. Lampert, “Maximum margin multi-label structured prediction,” presented at the NIPS: Neural Information Processing Systems, Granada, Spain, 2011.' ista: 'Lampert C. 2011. Maximum margin multi-label structured prediction. NIPS: Neural Information Processing Systems.' mla: Lampert, Christoph. Maximum Margin Multi-Label Structured Prediction. Neural Information Processing Systems, 2011. short: C. Lampert, in:, Neural Information Processing Systems, 2011. conference: end_date: 2011-12-14 location: Granada, Spain name: 'NIPS: Neural Information Processing Systems' start_date: 2011-12-12 date_created: 2018-12-11T12:01:45Z date_published: 2011-12-01T00:00:00Z date_updated: 2023-10-17T11:47:35Z day: '01' department: - _id: ChLa language: - iso: eng month: '12' oa_version: None publication_status: published publisher: Neural Information Processing Systems publist_id: '3522' quality_controlled: '1' related_material: record: - id: '3322' relation: later_version status: public scopus_import: 1 status: public title: Maximum margin multi-label structured prediction type: conference user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 year: '2011' ... --- _id: '3322' abstract: - lang: eng text: We study multi-label prediction for structured output spaces, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multi-label classification techniques are typically not applicable in this situation, because they require explicit enumeration of the label space, which is infeasible in case of structured outputs. Relying on techniques originally designed for single- label structured prediction, in particular structured support vector machines, results in reduced prediction accuracy, or leads to infeasible optimization problems. In this work we derive a maximum-margin training formulation for multi-label structured prediction that remains computationally tractable while achieving high prediction accuracy. It also shares most beneficial properties with single-label maximum-margin approaches, in particular a formulation as a convex optimization problem, efficient working set training, and PAC-Bayesian generalization bounds. article_processing_charge: No author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Lampert C. Maximum Margin Multi Label Structured Prediction. Neural Information Processing Systems Foundation; 2011. apa: 'Lampert, C. (2011). Maximum margin multi label structured prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems Foundation.' chicago: 'Lampert, Christoph. Maximum Margin Multi Label Structured Prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems Foundation, 2011.' ieee: C. Lampert, Maximum margin multi label structured prediction. Neural Information Processing Systems Foundation, 2011. ista: Lampert C. 2011. Maximum margin multi label structured prediction, Neural Information Processing Systems Foundation,p. mla: 'Lampert, Christoph. “Maximum Margin Multi Label Structured Prediction.” NIPS: Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2011.' short: C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information Processing Systems Foundation, 2011. date_created: 2018-12-11T12:02:40Z date_published: 2011-12-13T00:00:00Z date_updated: 2023-10-17T11:47:36Z day: '13' department: - _id: ChLa language: - iso: eng month: '12' oa_version: None publication: 'NIPS: Neural Information Processing Systems' publication_status: published publisher: Neural Information Processing Systems Foundation publist_id: '3313' related_material: record: - id: '3163' relation: earlier_version status: public status: public title: Maximum margin multi label structured prediction type: conference_poster user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2011' ... --- _id: '3320' abstract: - lang: eng text: Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. This monograph introduces the reader to the most popular classes of structured models in computer vision. Our focus is discrete undirected graphical models which we cover in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. We discuss separately recently successful techniques for prediction in general structured models. In the second part of this monograph we describe methods for parameter learning where we distinguish the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. We highlight developments to enhance current models and discuss kernelized models and latent variable models. To make the monograph more practical and to provide links to further study we provide examples of successful application of many methods in the computer vision literature. article_processing_charge: No article_type: original author: - first_name: Sebastian full_name: Nowozin, Sebastian last_name: Nowozin - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Nowozin S, Lampert C. Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. 2011;6(3-4):185-365. doi:10.1561/0600000033 apa: Nowozin, S., & Lampert, C. (2011). Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. Now Publishers. https://doi.org/10.1561/0600000033 chicago: Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction in Computer Vision.” Foundations and Trends in Computer Graphics and Vision. Now Publishers, 2011. https://doi.org/10.1561/0600000033. ieee: S. Nowozin and C. Lampert, “Structured learning and prediction in computer vision,” Foundations and Trends in Computer Graphics and Vision, vol. 6, no. 3–4. Now Publishers, pp. 185–365, 2011. ista: Nowozin S, Lampert C. 2011. Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. 6(3–4), 185–365. mla: Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction in Computer Vision.” Foundations and Trends in Computer Graphics and Vision, vol. 6, no. 3–4, Now Publishers, 2011, pp. 185–365, doi:10.1561/0600000033. short: S. Nowozin, C. Lampert, Foundations and Trends in Computer Graphics and Vision 6 (2011) 185–365. date_created: 2018-12-11T12:02:39Z date_published: 2011-05-23T00:00:00Z date_updated: 2023-10-17T11:52:46Z day: '23' ddc: - '000' department: - _id: ChLa doi: 10.1561/0600000033 file: - access_level: open_access checksum: f1043ef389f1558e2a226bb51568511f content_type: application/pdf creator: dernst date_created: 2020-05-14T14:34:47Z date_updated: 2020-07-14T12:46:07Z file_id: '7837' file_name: 2011_CompGraphicsVision_Nowozin.pdf file_size: 3745064 relation: main_file file_date_updated: 2020-07-14T12:46:07Z has_accepted_license: '1' intvolume: ' 6' issue: 3-4 language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: 185 - 365 publication: Foundations and Trends in Computer Graphics and Vision publication_status: published publisher: Now Publishers publist_id: '3315' quality_controlled: '1' scopus_import: '1' status: public title: Structured learning and prediction in computer vision type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 6 year: '2011' ... --- _id: '3319' abstract: - lang: eng text: We address the problem of metric learning for multi-view data, namely the construction of embedding projections from data in different representations into a shared feature space, such that the Euclidean distance in this space provides a meaningful within-view as well as between-view similarity. Our motivation stems from the problem of cross-media retrieval tasks, where the availability of a joint Euclidean distance function is a pre-requisite to allow fast, in particular hashing-based, nearest neighbor queries. We formulate an objective function that expresses the intuitive concept that matching samples are mapped closely together in the output space, whereas non-matching samples are pushed apart, no matter in which view they are available. The resulting optimization problem is not convex, but it can be decomposed explicitly into a convex and a concave part, thereby allowing efficient optimization using the convex-concave procedure. Experiments on an image retrieval task show that nearest-neighbor based cross-view retrieval is indeed possible, and the proposed technique improves the retrieval accuracy over baseline techniques. article_processing_charge: No author: - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Quadrianto N, Lampert C. Learning multi-view neighborhood preserving projections. In: ML Research Press; 2011:425-432.' apa: 'Quadrianto, N., & Lampert, C. (2011). Learning multi-view neighborhood preserving projections (pp. 425–432). Presented at the ICML: International Conference on Machine Learning, Bellevue, United States: ML Research Press.' chicago: Quadrianto, Novi, and Christoph Lampert. “Learning Multi-View Neighborhood Preserving Projections,” 425–32. ML Research Press, 2011. ieee: 'N. Quadrianto and C. Lampert, “Learning multi-view neighborhood preserving projections,” presented at the ICML: International Conference on Machine Learning, Bellevue, United States, 2011, pp. 425–432.' ista: 'Quadrianto N, Lampert C. 2011. Learning multi-view neighborhood preserving projections. ICML: International Conference on Machine Learning, 425–432.' mla: Quadrianto, Novi, and Christoph Lampert. Learning Multi-View Neighborhood Preserving Projections. ML Research Press, 2011, pp. 425–32. short: N. Quadrianto, C. Lampert, in:, ML Research Press, 2011, pp. 425–432. conference: end_date: 2011-07-02 location: Bellevue, United States name: 'ICML: International Conference on Machine Learning' start_date: 2011-06-28 date_created: 2018-12-11T12:02:39Z date_published: 2011-01-01T00:00:00Z date_updated: 2023-10-17T11:59:50Z day: '01' department: - _id: ChLa language: - iso: eng month: '01' oa_version: None page: 425 - 432 publication_status: published publisher: ML Research Press publist_id: '3316' scopus_import: '1' status: public title: Learning multi-view neighborhood preserving projections type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2011' ... --- _id: '3686' abstract: - lang: eng text: |+ Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to incorporating only local interactions and cannot model global properties such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deriving a potential function that forces the output labeling to be connected and that can naturally be used in the framework of recent maximum a posteriori (MAP)-MRF linear program (LP) relaxations. Using techniques from polyhedral combinatorics, we show that a provably strong approximation to the MAP solution of the resulting MRF can still be found efficiently by solving a sequence of max-flow problems. The efficiency of the inference procedure also allows us to learn the parameters of an MRF with global connectivity potentials by means of a cutting plane algorithm. We experimentally evaluate our algorithm on both synthetic data and on the challenging image segmentation task of the PASCAL Visual Object Classes 2008 data set. We show that in both cases the addition of a connectedness prior significantly reduces the segmentation error. acknowledgement: This work was funded in part by the EU CLASS project, IST 027978. This work was also supported in part by the IST Programme of the European Community under the PASCAL Network of Excellence, IST-2002-506778. author: - first_name: Sebastian full_name: Nowozin, Sebastian last_name: Nowozin - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Nowozin S, Lampert C. Global interactions in random field models: A potential function ensuring connectedness. SIAM Journal on Imaging Sciences. 2010;3(4 (Special Section on Optimization in Imaging Sciences)):1048-1074. doi:10.1137/090752614' apa: 'Nowozin, S., & Lampert, C. (2010). Global interactions in random field models: A potential function ensuring connectedness. SIAM Journal on Imaging Sciences. Society for Industrial and Applied Mathematics . https://doi.org/10.1137/090752614' chicago: 'Nowozin, Sebastian, and Christoph Lampert. “Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness.” SIAM Journal on Imaging Sciences. Society for Industrial and Applied Mathematics , 2010. https://doi.org/10.1137/090752614.' ieee: 'S. Nowozin and C. Lampert, “Global interactions in random field models: A potential function ensuring connectedness,” SIAM Journal on Imaging Sciences, vol. 3, no. 4 (Special Section on Optimization in Imaging Sciences). Society for Industrial and Applied Mathematics , pp. 1048–1074, 2010.' ista: 'Nowozin S, Lampert C. 2010. Global interactions in random field models: A potential function ensuring connectedness. SIAM Journal on Imaging Sciences. 3(4 (Special Section on Optimization in Imaging Sciences)), 1048–1074.' mla: 'Nowozin, Sebastian, and Christoph Lampert. “Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness.” SIAM Journal on Imaging Sciences, vol. 3, no. 4 (Special Section on Optimization in Imaging Sciences), Society for Industrial and Applied Mathematics , 2010, pp. 1048–74, doi:10.1137/090752614.' short: S. Nowozin, C. Lampert, SIAM Journal on Imaging Sciences 3 (2010) 1048–1074. date_created: 2018-12-11T12:04:37Z date_published: 2010-12-21T00:00:00Z date_updated: 2021-01-12T07:48:57Z day: '21' doi: 10.1137/090752614 extern: 1 intvolume: ' 3' issue: 4 (Special Section on Optimization in Imaging Sciences) month: '12' page: 1048 - 1074 publication: SIAM Journal on Imaging Sciences publication_status: published publisher: 'Society for Industrial and Applied Mathematics ' publist_id: '2684' quality_controlled: 0 status: public title: 'Global interactions in random field models: A potential function ensuring connectedness' type: journal_article volume: 3 year: '2010' ... ... --- _id: '3682' abstract: - lang: eng text: For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional class. One-shot recognition aims to apply the knowledge gained from a set of categories with plentiful data to categories for which only a single exemplar is available for each. As with earlier efforts motivated by transfer learning, we seek an internal representation for the domain that generalizes across classes. However, in contrast to existing work, we formulate the problem in a fundamentally new manner by optimizing the internal representation for the one-shot task using the notion of micro-sets. A micro-set is a sample of data that contains only a single instance of each category, sampled from the pool of available data, which serves as a mechanism to force the learned representation to explicitly address the variability and noise inherent in the one-shot recognition task. We optimize our learned domain features so that they minimize an expected loss over micro-sets drawn from the training set and show that these features generalize effectively to previously unseen categories. We detail a discriminative approach for optimizing one-shot recognition using micro-sets and present experiments on the Animals with Attributes and Caltech-101 datasets that demonstrate the benefits of our formulation. author: - first_name: Kevin full_name: Tang, Kevin D last_name: Tang - first_name: Marshall full_name: Tappen, Marshall F last_name: Tappen - first_name: Rahul full_name: Sukthankar,Rahul last_name: Sukthankar - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Tang K, Tappen M, Sukthankar R, Lampert C. Optimizing one-shot recognition with micro-set learning. In: IEEE; 2010:3027-3034. doi:10.1109/CVPR.2010.5540053' apa: 'Tang, K., Tappen, M., Sukthankar, R., & Lampert, C. (2010). Optimizing one-shot recognition with micro-set learning (pp. 3027–3034). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. https://doi.org/10.1109/CVPR.2010.5540053' chicago: Tang, Kevin, Marshall Tappen, Rahul Sukthankar, and Christoph Lampert. “Optimizing One-Shot Recognition with Micro-Set Learning,” 3027–34. IEEE, 2010. https://doi.org/10.1109/CVPR.2010.5540053. ieee: 'K. Tang, M. Tappen, R. Sukthankar, and C. Lampert, “Optimizing one-shot recognition with micro-set learning,” presented at the CVPR: Computer Vision and Pattern Recognition, 2010, pp. 3027–3034.' ista: 'Tang K, Tappen M, Sukthankar R, Lampert C. 2010. Optimizing one-shot recognition with micro-set learning. CVPR: Computer Vision and Pattern Recognition, 3027–3034.' mla: Tang, Kevin, et al. Optimizing One-Shot Recognition with Micro-Set Learning. IEEE, 2010, pp. 3027–34, doi:10.1109/CVPR.2010.5540053. short: K. Tang, M. Tappen, R. Sukthankar, C. Lampert, in:, IEEE, 2010, pp. 3027–3034. conference: name: 'CVPR: Computer Vision and Pattern Recognition' date_created: 2018-12-11T12:04:36Z date_published: 2010-06-18T00:00:00Z date_updated: 2021-01-12T07:45:06Z day: '18' doi: 10.1109/CVPR.2010.5540053 extern: 1 month: '06' page: 3027 - 3034 publication_status: published publisher: IEEE publist_id: '2696' quality_controlled: 0 status: public title: Optimizing one-shot recognition with micro-set learning type: conference year: '2010' ... --- _id: '3702' abstract: - lang: eng text: Hitting and batting tasks, such as tennis forehands, ping-pong strokes, or baseball batting, depend on predictions where the ball can be intercepted and how it can properly be returned to the opponent. These predictions get more accurate over time, hence the behaviors need to be continuously modified. As a result, movement templates with a learned global shape need to be adapted during the execution so that the racket reaches a target position and velocity that will return the ball over to the other side of the net or court. It requires altering learned movements to hit a varying target with the necessary velocity at a specific instant in time. Such a task cannot be incorporated straightforwardly in most movement representations suitable for learning. For example, the standard formulation of the dynamical system based motor primitives (introduced by Ijspeert et al. [1]) does not satisfy this property despite their flexibility which has allowed learning tasks ranging from locomotion to kendama. In order to fulfill this requirement, we reformulate the Ijspeert framework to incorporate the possibility of specifying a desired hitting point and a desired hitting velocity while maintaining all advantages of the original formulation. We show that the proposed movement template formulation works well in two scenarios, i.e., for hitting a ball on a string with a table tennis racket at a specified velocity and for returning balls launched by a ball gun successfully over the net using forehand movements. All experiments were carried out on a Barrett WAM using a four camera vision system. author: - first_name: Jens full_name: Kober,Jens last_name: Kober - first_name: Katharina full_name: Mülling,Katharina last_name: Mülling - first_name: Oliver full_name: Krömer,Oliver last_name: Krömer - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Bernhard full_name: Schölkopf,Bernhard last_name: Schölkopf - first_name: Jan full_name: Peters, Jan last_name: Peters citation: ama: 'Kober J, Mülling K, Krömer O, Lampert C, Schölkopf B, Peters J. Movement templates for learning of hitting and batting. In: IEEE; 2010:853-858. doi:10.1109/ROBOT.2010.5509672' apa: 'Kober, J., Mülling, K., Krömer, O., Lampert, C., Schölkopf, B., & Peters, J. (2010). Movement templates for learning of hitting and batting (pp. 853–858). Presented at the ICRA: International Conference on Robotics and Automation, IEEE. https://doi.org/10.1109/ROBOT.2010.5509672' chicago: Kober, Jens, Katharina Mülling, Oliver Krömer, Christoph Lampert, Bernhard Schölkopf, and Jan Peters. “Movement Templates for Learning of Hitting and Batting,” 853–58. IEEE, 2010. https://doi.org/10.1109/ROBOT.2010.5509672. ieee: 'J. Kober, K. Mülling, O. Krömer, C. Lampert, B. Schölkopf, and J. Peters, “Movement templates for learning of hitting and batting,” presented at the ICRA: International Conference on Robotics and Automation, 2010, pp. 853–858.' ista: 'Kober J, Mülling K, Krömer O, Lampert C, Schölkopf B, Peters J. 2010. Movement templates for learning of hitting and batting. ICRA: International Conference on Robotics and Automation, 853–858.' mla: Kober, Jens, et al. Movement Templates for Learning of Hitting and Batting. IEEE, 2010, pp. 853–58, doi:10.1109/ROBOT.2010.5509672. short: J. Kober, K. Mülling, O. Krömer, C. Lampert, B. Schölkopf, J. Peters, in:, IEEE, 2010, pp. 853–858. conference: name: 'ICRA: International Conference on Robotics and Automation' date_created: 2018-12-11T12:04:42Z date_published: 2010-05-07T00:00:00Z date_updated: 2021-01-12T07:51:35Z day: '07' doi: 10.1109/ROBOT.2010.5509672 extern: 1 main_file_link: - open_access: '0' url: http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/ICRA2010-Kober_6231%5b1%5d.pdf month: '05' page: 853 - 858 publication_status: published publisher: IEEE publist_id: '2654' quality_controlled: 0 status: public title: Movement templates for learning of hitting and batting type: conference year: '2010' ... --- _id: '3794' abstract: - lang: eng text: 'We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.' alternative_title: - LNCS author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Oliver full_name: Krömer, Oliver last_name: Krömer citation: ama: 'Lampert C, Krömer O. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. In: Vol 6312. Springer; 2010:566-579. doi:10.1007/978-3-642-15552-9_41' apa: 'Lampert, C., & Krömer, O. (2010). Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp. 566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15552-9_41' chicago: Lampert, Christoph, and Oliver Krömer. “Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning,” 6312:566–79. Springer, 2010. https://doi.org/10.1007/978-3-642-15552-9_41. ieee: 'C. Lampert and O. Krömer, “Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning,” presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6312, pp. 566–579.' ista: 'Lampert C, Krömer O. 2010. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. ECCV: European Conference on Computer Vision, LNCS, vol. 6312, 566–579.' mla: Lampert, Christoph, and Oliver Krömer. Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning. Vol. 6312, Springer, 2010, pp. 566–79, doi:10.1007/978-3-642-15552-9_41. short: C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579. conference: end_date: 2010-09-11 location: Heraklion, Crete, Greece name: 'ECCV: European Conference on Computer Vision' start_date: 2010-09-05 date_created: 2018-12-11T12:05:12Z date_published: 2010-11-10T00:00:00Z date_updated: 2021-01-12T07:52:14Z day: '10' department: - _id: ChLa doi: 10.1007/978-3-642-15552-9_41 intvolume: ' 6312' language: - iso: eng main_file_link: - url: http://www.ics.forth.gr/eccv2010/intro.php month: '11' oa_version: None page: 566 - 579 publication_status: published publisher: Springer publist_id: '2433' quality_controlled: '1' scopus_import: 1 status: public title: Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 6312 year: '2010' ... --- _id: '3676' abstract: - lang: eng text: |- Most state-of-the-art systems for content-based video understanding tasks require video content to be represented as collections of many low-level descriptors, e.g. as histograms of the color, texture or motion in local image regions. In order to preserve as much of the information contained in the original video as possible, these representations are typically high-dimensional, which conflicts with the aim for compact descriptors that would allow better efficiency and lower storage requirements. In this paper, we address the problem of semantic com- pression of video, i.e. the reduction of low-level descriptors to a small number of dimensions while preserving most of the semantic information. For this, we adapt topic models – which have previously been used as compact representations of still images – to take into account the temporal structure of a video, as well as multi-modal components such as motion information. Experiments on a large-scale collection of YouTube videos show that we can achieve a compression ratio of 20 : 1 compared to ordinary histogram representations and at least 2 : 1 compared to other dimensionality reduction techniques without significant loss of prediction accuracy. Also, improvements are demonstrated for our video-specific extensions modeling temporal structure and multiple modalities. author: - first_name: Jörn full_name: Wanke,Jörn last_name: Wanke - first_name: Adrian full_name: Ulges, Adrian last_name: Ulges - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Thomas full_name: Breuel,Thomas M last_name: Breuel citation: ama: 'Wanke J, Ulges A, Lampert C, Breuel T. Topic models for semantic video compression. In: ACM; 2010:275-284. doi:10.1145/1743384.1743433' apa: 'Wanke, J., Ulges, A., Lampert, C., & Breuel, T. (2010). Topic models for semantic video compression (pp. 275–284). Presented at the MIR: Multimedia Information Retrieval, ACM. https://doi.org/10.1145/1743384.1743433' chicago: Wanke, Jörn, Adrian Ulges, Christoph Lampert, and Thomas Breuel. “Topic Models for Semantic Video Compression,” 275–84. ACM, 2010. https://doi.org/10.1145/1743384.1743433. ieee: 'J. Wanke, A. Ulges, C. Lampert, and T. Breuel, “Topic models for semantic video compression,” presented at the MIR: Multimedia Information Retrieval, 2010, pp. 275–284.' ista: 'Wanke J, Ulges A, Lampert C, Breuel T. 2010. Topic models for semantic video compression. MIR: Multimedia Information Retrieval, 275–284.' mla: Wanke, Jörn, et al. Topic Models for Semantic Video Compression. ACM, 2010, pp. 275–84, doi:10.1145/1743384.1743433. short: J. Wanke, A. Ulges, C. Lampert, T. Breuel, in:, ACM, 2010, pp. 275–284. conference: name: 'MIR: Multimedia Information Retrieval' date_created: 2018-12-11T12:04:34Z date_published: 2010-03-31T00:00:00Z date_updated: 2021-01-12T07:45:04Z day: '31' doi: 10.1145/1743384.1743433 extern: 1 main_file_link: - open_access: '0' url: http://pub.ist.ac.at/~chl/papers/wanke-mir2010.pdf month: '03' page: 275 - 284 publication_status: published publisher: ACM publist_id: '2705' quality_controlled: 0 status: public title: Topic models for semantic video compression type: conference year: '2010' ... --- _id: '3697' abstract: - lang: eng text: The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed. acknowledgement: The authors acknowledge support from the EU projects CLASS (IST project 027978), PerAct (IST project 504321) and the EU Network of Excellence PASCAL2. author: - first_name: Tinne full_name: Tuytelaars,Tinne last_name: Tuytelaars - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Matthew full_name: Blaschko,Matthew B last_name: Blaschko - first_name: Wray full_name: Buntine,Wray last_name: Buntine citation: ama: 'Tuytelaars T, Lampert C, Blaschko M, Buntine W. Unsupervised object discovery: A comparison. International Journal of Computer Vision. 2010;88(2):284-302. doi:10.1007/s11263-009-0271-8' apa: 'Tuytelaars, T., Lampert, C., Blaschko, M., & Buntine, W. (2010). Unsupervised object discovery: A comparison. International Journal of Computer Vision. Springer. https://doi.org/10.1007/s11263-009-0271-8' chicago: 'Tuytelaars, Tinne, Christoph Lampert, Matthew Blaschko, and Wray Buntine. “Unsupervised Object Discovery: A Comparison.” International Journal of Computer Vision. Springer, 2010. https://doi.org/10.1007/s11263-009-0271-8.' ieee: 'T. Tuytelaars, C. Lampert, M. Blaschko, and W. Buntine, “Unsupervised object discovery: A comparison,” International Journal of Computer Vision, vol. 88, no. 2. Springer, pp. 284–302, 2010.' ista: 'Tuytelaars T, Lampert C, Blaschko M, Buntine W. 2010. Unsupervised object discovery: A comparison. International Journal of Computer Vision. 88(2), 284–302.' mla: 'Tuytelaars, Tinne, et al. “Unsupervised Object Discovery: A Comparison.” International Journal of Computer Vision, vol. 88, no. 2, Springer, 2010, pp. 284–302, doi:10.1007/s11263-009-0271-8.' short: T. Tuytelaars, C. Lampert, M. Blaschko, W. Buntine, International Journal of Computer Vision 88 (2010) 284–302. date_created: 2018-12-11T12:04:40Z date_published: 2010-06-01T00:00:00Z date_updated: 2021-01-12T07:49:02Z day: '01' doi: 10.1007/s11263-009-0271-8 extern: 1 intvolume: ' 88' issue: '2' license: https://creativecommons.org/licenses/by-nc/4.0/ month: '06' page: 284 - 302 publication: International Journal of Computer Vision publication_status: published publisher: Springer publist_id: '2664' quality_controlled: 0 status: public title: 'Unsupervised object discovery: A comparison' tmp: image: /images/cc_by_nc.png legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) short: CC BY-NC (4.0) type: journal_article volume: 88 year: '2010' ... --- _id: '3713' abstract: - lang: eng text: We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-and-conquer procedure in the space of candidate regions. Compared to the exhaustive procedure that thus far is the state-of-the-art for cascade evaluation, the proposed method requires fewer evaluations of the classifier functions, thereby speeding up the search. Furthermore, we show how the recently developed efficient subwindow search (ESS) procedure [11] can be integrated into the last stage of our method. This allows us to use our method to act not only as a faster procedure for cascade evaluation, but also as a tool to perform efficient branch-and-bound object detection with nonlinear quality functions, in particular kernelized support vector machines. Experiments on the PASCAL VOC 2006 dataset show an acceleration of more than 50% by our method compared to standard cascade evaluation. acknowledgement: |- Conference Information URL: http://cvl.umiacs.umd.edu/conferences/cvpr2010/ author: - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Lampert C. An efficient divide-and-conquer cascade for nonlinear object detection. In: IEEE; 2010:1022-1029. doi:10.1109/CVPR.2010.5540107' apa: 'Lampert, C. (2010). An efficient divide-and-conquer cascade for nonlinear object detection (pp. 1022–1029). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. https://doi.org/10.1109/CVPR.2010.5540107' chicago: Lampert, Christoph. “An Efficient Divide-and-Conquer Cascade for Nonlinear Object Detection,” 1022–29. IEEE, 2010. https://doi.org/10.1109/CVPR.2010.5540107. ieee: 'C. Lampert, “An efficient divide-and-conquer cascade for nonlinear object detection,” presented at the CVPR: Computer Vision and Pattern Recognition, 2010, pp. 1022–1029.' ista: 'Lampert C. 2010. An efficient divide-and-conquer cascade for nonlinear object detection. CVPR: Computer Vision and Pattern Recognition, 1022–1029.' mla: Lampert, Christoph. An Efficient Divide-and-Conquer Cascade for Nonlinear Object Detection. IEEE, 2010, pp. 1022–29, doi:10.1109/CVPR.2010.5540107. short: C. Lampert, in:, IEEE, 2010, pp. 1022–1029. conference: name: 'CVPR: Computer Vision and Pattern Recognition' date_created: 2018-12-11T12:04:46Z date_published: 2010-06-18T00:00:00Z date_updated: 2021-01-12T07:51:40Z day: '18' doi: 10.1109/CVPR.2010.5540107 extern: 1 month: '06' page: 1022 - 1029 publication_status: published publisher: IEEE publist_id: '2643' quality_controlled: 0 status: public title: An efficient divide-and-conquer cascade for nonlinear object detection type: conference year: '2010' ... --- _id: '3793' abstract: - lang: eng text: "Recent progress in per-pixel object class labeling of natural images can be attributed to the use of multiple types of image features and sound statistical learning approaches. Within the latter, Conditional Random Fields (CRF) are prominently used for their ability to represent interactions between random variables. Despite their popularity in computer vision, parameter learning for CRFs has remained difficult, popular approaches being cross-validation and piecewise training.\r\nIn this work, we propose a simple yet expressive tree-structured CRF based on a recent hierarchical image segmentation method. Our model combines and weights multiple image features within a hierarchical representation and allows simple and efficient globally-optimal learning of ≈ 105 parameters. The tractability of our model allows us to pose and answer some of the open questions regarding parameter learning applying to CRF-based approaches. The key findings for learning CRF models are, from the obvious to the surprising, i) multiple image features always help, ii) the limiting dimension with respect to current models is the amount of training data, iii) piecewise training is competitive, iv) current methods for max-margin training fail for models with many parameters.\r\n" alternative_title: - LNCS article_processing_charge: No author: - first_name: Sebastian full_name: Nowozin, Sebastian last_name: Nowozin - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Nowozin S, Gehler P, Lampert C. On parameter learning in CRF-based approaches to object class image segmentation. In: Vol 6316. Springer; 2010:98-111. doi:10.1007/978-3-642-15567-3_8' apa: 'Nowozin, S., Gehler, P., & Lampert, C. (2010). On parameter learning in CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15567-3_8' chicago: Nowozin, Sebastian, Peter Gehler, and Christoph Lampert. “On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation,” 6316:98–111. Springer, 2010. https://doi.org/10.1007/978-3-642-15567-3_8. ieee: 'S. Nowozin, P. Gehler, and C. Lampert, “On parameter learning in CRF-based approaches to object class image segmentation,” presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6316, pp. 98–111.' ista: 'Nowozin S, Gehler P, Lampert C. 2010. On parameter learning in CRF-based approaches to object class image segmentation. ECCV: European Conference on Computer Vision, LNCS, vol. 6316, 98–111.' mla: Nowozin, Sebastian, et al. On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation. Vol. 6316, Springer, 2010, pp. 98–111, doi:10.1007/978-3-642-15567-3_8. short: S. Nowozin, P. Gehler, C. Lampert, in:, Springer, 2010, pp. 98–111. conference: end_date: 2010-09-11 location: Heraklion, Crete, Greece name: 'ECCV: European Conference on Computer Vision' start_date: 2010-09-05 date_created: 2018-12-11T12:05:12Z date_published: 2010-11-04T00:00:00Z date_updated: 2021-01-12T07:52:14Z day: '04' ddc: - '000' department: - _id: ChLa doi: 10.1007/978-3-642-15567-3_8 file: - access_level: open_access checksum: 3716e10e161f7c714fd17ec193a223c3 content_type: application/pdf creator: dernst date_created: 2020-05-19T16:27:34Z date_updated: 2020-07-14T12:46:16Z file_id: '7871' file_name: 2010_ECCV_Nowozin.pdf file_size: 4087332 relation: main_file file_date_updated: 2020-07-14T12:46:16Z has_accepted_license: '1' intvolume: ' 6316' language: - iso: eng month: '11' oa: 1 oa_version: Submitted Version page: 98 - 111 publication_status: published publisher: Springer publist_id: '2431' quality_controlled: '1' scopus_import: 1 status: public title: On parameter learning in CRF-based approaches to object class image segmentation type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 6316 year: '2010' ... --- _id: '3699' abstract: - lang: eng text: Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, CCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, CCA may suffer from small sample effects. We propose to use semisupervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of CCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of CCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing. author: - first_name: Matthew full_name: Blaschko,Matthew B last_name: Blaschko - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Andreas full_name: Bartels, Andreas last_name: Bartels citation: ama: Blaschko M, Lampert C, Bartels A. Semi-Supervised Analysis of Human FMRI Data. Berlin Institute of Technology; 2009. apa: 'Blaschko, M., Lampert, C., & Bartels, A. (2009). Semi-supervised analysis of human fMRI data. BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology. Berlin Institute of Technology.' chicago: 'Blaschko, Matthew, Christoph Lampert, and Andreas Bartels. Semi-Supervised Analysis of Human FMRI Data. BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology. Berlin Institute of Technology, 2009.' ieee: M. Blaschko, C. Lampert, and A. Bartels, Semi-supervised analysis of human fMRI data. Berlin Institute of Technology, 2009. ista: Blaschko M, Lampert C, Bartels A. 2009. Semi-supervised analysis of human fMRI data, Berlin Institute of Technology,p. mla: 'Blaschko, Matthew, et al. “Semi-Supervised Analysis of Human FMRI Data.” BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology, Berlin Institute of Technology, 2009.' short: M. Blaschko, C. Lampert, A. Bartels, Semi-Supervised Analysis of Human FMRI Data, Berlin Institute of Technology, 2009. date_created: 2018-12-11T12:04:41Z date_published: 2009-07-10T00:00:00Z date_updated: 2019-04-26T07:22:33Z day: '10' extern: 1 main_file_link: - open_access: '0' url: http://pubman.mpdl.mpg.de/pubman/faces/viewItemOverviewPage.jsp?itemId=escidoc:1789281 month: '07' publication: 'BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology' publication_status: published publisher: Berlin Institute of Technology publist_id: '2661' quality_controlled: 0 status: public title: Semi-supervised analysis of human fMRI data type: conference_poster year: '2009' ... --- _id: '3703' abstract: - lang: eng text: Recent research has shown that the use of contextual cues significantly improves performance in sliding window type localization systems. In this work, we propose a method that incorporates both global and local context information through appropriately defined kernel functions. In particular, we make use of a weighted combination of kernels defined over local spatial regions, as well as a global context kernel. The relative importance of the context contributions is learned automatically, and the resulting discriminant function is of a form such that localization at test time can be solved efficiently using a branch and bound optimization scheme. By specifying context directly with a kernel learning approach, we achieve high localization accuracy with a simple and efficient representation. This is in contrast to other systems that incorporate context for which expensive inference needs to be done at test time. We show experimentally on the PASCAL VOC datasets that the inclusion of context can significantly improve localization performance, provided the relative contributions of context cues are learned appropriately. acknowledgement: The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007- 2013) / ERC grant agreement no. 228180. This work was funded in part by the EC project CLASS, IST 027978, and the PASCAL2 network of excellence. The first author is supported by the Royal Academy of Engineering through a Newton International Fellowship. alternative_title: - Proceedings of the BMVC author: - first_name: Matthew full_name: Blaschko,Matthew B last_name: Blaschko - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Blaschko M, Lampert C. Object localization with global and local context kernels. In: BMVA Press; 2009:1-11. doi:10.5244/C.23.63' apa: 'Blaschko, M., & Lampert, C. (2009). Object localization with global and local context kernels (pp. 1–11). Presented at the BMVC: British Machine Vision Conference, BMVA Press. https://doi.org/10.5244/C.23.63' chicago: Blaschko, Matthew, and Christoph Lampert. “Object Localization with Global and Local Context Kernels,” 1–11. BMVA Press, 2009. https://doi.org/10.5244/C.23.63. ieee: 'M. Blaschko and C. Lampert, “Object localization with global and local context kernels,” presented at the BMVC: British Machine Vision Conference, 2009, pp. 1–11.' ista: 'Blaschko M, Lampert C. 2009. Object localization with global and local context kernels. BMVC: British Machine Vision Conference, Proceedings of the BMVC, , 1–11.' mla: Blaschko, Matthew, and Christoph Lampert. Object Localization with Global and Local Context Kernels. BMVA Press, 2009, pp. 1–11, doi:10.5244/C.23.63. short: M. Blaschko, C. Lampert, in:, BMVA Press, 2009, pp. 1–11. conference: name: 'BMVC: British Machine Vision Conference' date_created: 2018-12-11T12:04:42Z date_published: 2009-09-10T00:00:00Z date_updated: 2021-01-12T07:51:36Z day: '10' doi: 10.5244/C.23.63 extern: 1 main_file_link: - open_access: '0' url: http://www.bmva.org/bmvc/2009/Papers/Paper228/Paper228.pdf month: '09' page: 1 - 11 publication_status: published publisher: BMVA Press publist_id: '2655' quality_controlled: 0 status: public title: Object localization with global and local context kernels tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference year: '2009' ... --- _id: '3704' abstract: - lang: eng text: We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, ldquoAnimals with Attributesrdquo, of over 30,000 animal images that match the 50 classes in Osherson‘s classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes. acknowledgement: This work was funded in part by the EC project CLASS, IST 027978, and the PASCAL2 network of excellence. author: - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Hannes full_name: Nickisch,Hannes last_name: Nickisch - first_name: Stefan full_name: Harmeling,Stefan last_name: Harmeling citation: ama: 'Lampert C, Nickisch H, Harmeling S. Learning to detect unseen object classes by between-class attribute transfer. In: IEEE; 2009:951-958. doi:10.1109/CVPR.2009.5206594' apa: 'Lampert, C., Nickisch, H., & Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer (pp. 951–958). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. https://doi.org/10.1109/CVPR.2009.5206594' chicago: Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer,” 951–58. IEEE, 2009. https://doi.org/10.1109/CVPR.2009.5206594. ieee: 'C. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, pp. 951–958.' ista: 'Lampert C, Nickisch H, Harmeling S. 2009. Learning to detect unseen object classes by between-class attribute transfer. CVPR: Computer Vision and Pattern Recognition, 951–958.' mla: Lampert, Christoph, et al. Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer. IEEE, 2009, pp. 951–58, doi:10.1109/CVPR.2009.5206594. short: C. Lampert, H. Nickisch, S. Harmeling, in:, IEEE, 2009, pp. 951–958. conference: name: 'CVPR: Computer Vision and Pattern Recognition' date_created: 2018-12-11T12:04:43Z date_published: 2009-06-20T00:00:00Z date_updated: 2021-01-12T07:51:36Z day: '20' doi: 10.1109/CVPR.2009.5206594 extern: 1 month: '06' page: 951 - 958 publication_status: published publisher: IEEE publist_id: '2652' quality_controlled: 0 status: public title: Learning to detect unseen object classes by between-class attribute transfer type: conference year: '2009' ... --- _id: '3715' abstract: - lang: eng text: High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human motion analysis. However, building a system that can adapt to arbitrary objects and a wide range of lighting conditions is a challenging problem, especially if hard real-time constraints apply like in robotics scenarios. In this work, we introduce a method for learning a discriminative object tracking system based on the recent structured regression framework for object localization. Using a kernel function that allows fast evaluation on the GPU, the resulting system can process video streams at speed of 100 frames per second or more. Consecutive frames in high speed video sequences are typically very redundant, and for training an object detection system, it is sufficient to have training labels from only a subset of all images. We propose an active learning method that select training examples in a data-driven way, thereby minimizing the required number of training labeling. Experiments on realistic data show that the active learning is superior to previously used methods for dataset subsampling for this task. acknowledgement: |- This work was funded in part by the EU project CLASS, IST 027978. Conference Information URL: http://www.optecnet.de/veranstaltungen/2009/09/dagm-2009/ alternative_title: - LNCS author: - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Jan full_name: Peters, Jan last_name: Peters citation: ama: 'Lampert C, Peters J. Active structured learning for high-speed object detection. In: Vol 5748. Springer; 2009:221-231. doi:10.1007/978-3-642-03798-6_23' apa: 'Lampert, C., & Peters, J. (2009). Active structured learning for high-speed object detection (Vol. 5748, pp. 221–231). Presented at the DAGM: German Association For Pattern Recognition, Springer. https://doi.org/10.1007/978-3-642-03798-6_23' chicago: Lampert, Christoph, and Jan Peters. “Active Structured Learning for High-Speed Object Detection,” 5748:221–31. Springer, 2009. https://doi.org/10.1007/978-3-642-03798-6_23. ieee: 'C. Lampert and J. Peters, “Active structured learning for high-speed object detection,” presented at the DAGM: German Association For Pattern Recognition, 2009, vol. 5748, pp. 221–231.' ista: 'Lampert C, Peters J. 2009. Active structured learning for high-speed object detection. DAGM: German Association For Pattern Recognition, LNCS, vol. 5748, 221–231.' mla: Lampert, Christoph, and Jan Peters. Active Structured Learning for High-Speed Object Detection. Vol. 5748, Springer, 2009, pp. 221–31, doi:10.1007/978-3-642-03798-6_23. short: C. Lampert, J. Peters, in:, Springer, 2009, pp. 221–231. conference: name: 'DAGM: German Association For Pattern Recognition' date_created: 2018-12-11T12:04:46Z date_published: 2009-10-07T00:00:00Z date_updated: 2021-01-12T07:51:41Z day: '07' doi: 10.1007/978-3-642-03798-6_23 extern: 1 intvolume: ' 5748' month: '10' page: 221 - 231 publication_status: published publisher: Springer publist_id: '2642' quality_controlled: 0 status: public title: Active structured learning for high-speed object detection type: conference volume: 5748 year: '2009' ... --- _id: '3717' abstract: - lang: eng text: We introduce RTblob, an open-source real-time vision system for 3D object detection that achieves over 200 Hz tracking speed with only off-the-shelf hardware component. It allows fast and accurate tracking of colored objects in 3D without expensive and often custom-built hardware, instead making use of the PC graphics cards for the necessary image processing operations. acknowledgement: 'IEEE Workshop URL: http://humanoidscv.ime.cmc.osaka-u.ac.jp/' author: - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Jan full_name: Peters, Jan last_name: Peters citation: ama: Lampert C, Peters J. A High-Speed Object Tracker from off-the-Shelf Components. IEEE; 2009. apa: 'Lampert, C., & Peters, J. (2009). A high-speed object tracker from off-the-shelf components. ICCV: International Conference on Computer Vision. IEEE.' chicago: 'Lampert, Christoph, and Jan Peters. A High-Speed Object Tracker from off-the-Shelf Components. ICCV: International Conference on Computer Vision. IEEE, 2009.' ieee: C. Lampert and J. Peters, A high-speed object tracker from off-the-shelf components. IEEE, 2009. ista: Lampert C, Peters J. 2009. A high-speed object tracker from off-the-shelf components, IEEE,p. mla: 'Lampert, Christoph, and Jan Peters. “A High-Speed Object Tracker from off-the-Shelf Components.” ICCV: International Conference on Computer Vision, IEEE, 2009.' short: C. Lampert, J. Peters, A High-Speed Object Tracker from off-the-Shelf Components, IEEE, 2009. date_created: 2018-12-11T12:04:47Z date_published: 2009-09-27T00:00:00Z date_updated: 2020-07-14T12:46:14Z day: '27' extern: 1 main_file_link: - open_access: '0' url: http://pubman.mpdl.mpg.de/pubman/faces/viewItemOverviewPage.jsp?itemId=escidoc:1789154 month: '09' publication: 'ICCV: International Conference on Computer Vision' publication_status: published publisher: IEEE publist_id: '2640' quality_controlled: 0 status: public title: A high-speed object tracker from off-the-shelf components tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference_poster year: '2009' ... --- _id: '3696' abstract: - lang: eng text: Discriminative techniques, such as conditional random fields (CRFs) or structure aware maximum-margin techniques (maximum margin Markov networks (M3N), structured output support vector machines (S-SVM)), are state-of-the-art in the prediction of structured data. However, to achieve good results these techniques require complete and reliable ground truth, which is not always available in realistic problems. Furthermore, training either CRFs or margin-based techniques is computationally costly, because the runtime of current training methods depends not only on the size of the training set but also on properties of the output space to which the training samples are assigned. We propose an alternative model for structured output prediction, Joint Kernel Support Estimation (JKSE), which is rather generative in nature as it relies on estimating the joint probability density of samples and labels in the training set. This makes it tolerant against incomplete or incorrect labels and also opens the possibility of learning in situations where more than one output label can be considered correct. At the same time, we avoid typical problems of generative models as we do not attempt to learn the full joint probability distribution, but we model only its support in a joint reproducing kernel Hilbert space. As a consequence, JKSE training is possible by an adaption of the classical one-class SVM procedure. The resulting optimization problem is convex and efficiently solvable even with tens of thousands of training examples. A particular advantage of JKSE is that the training speed depends only on the size of the training set, and not on the total size of the label space. No inference step during training is required (as M3N and S-SVM would) nor do we have calculate a partition function (as CRFs do). Experiments on realistic data show that, for suitable kernel functions, our method works efficiently and robustly in situations that discriminative techniques have problems with or that are computationally infeasible for them. author: - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Matthew full_name: Blaschko,Matthew B last_name: Blaschko citation: ama: Lampert C, Blaschko M. Structured prediction by joint kernel support estimation. Machine Learning. 2009;77(2-3):249-269. doi:10.1007/s10994-009-5111-0 apa: Lampert, C., & Blaschko, M. (2009). Structured prediction by joint kernel support estimation. Machine Learning. Springer. https://doi.org/10.1007/s10994-009-5111-0 chicago: Lampert, Christoph, and Matthew Blaschko. “Structured Prediction by Joint Kernel Support Estimation.” Machine Learning. Springer, 2009. https://doi.org/10.1007/s10994-009-5111-0. ieee: C. Lampert and M. Blaschko, “Structured prediction by joint kernel support estimation,” Machine Learning, vol. 77, no. 2–3. Springer, pp. 249–269, 2009. ista: Lampert C, Blaschko M. 2009. Structured prediction by joint kernel support estimation. Machine Learning. 77(2–3), 249–269. mla: Lampert, Christoph, and Matthew Blaschko. “Structured Prediction by Joint Kernel Support Estimation.” Machine Learning, vol. 77, no. 2–3, Springer, 2009, pp. 249–69, doi:10.1007/s10994-009-5111-0. short: C. Lampert, M. Blaschko, Machine Learning 77 (2009) 249–269. date_created: 2018-12-11T12:04:40Z date_published: 2009-04-07T00:00:00Z date_updated: 2021-01-12T07:49:01Z day: '07' doi: 10.1007/s10994-009-5111-0 extern: 1 intvolume: ' 77' issue: 2-3 month: '04' page: 249 - 269 publication: Machine Learning publication_status: published publisher: Springer publist_id: '2663' quality_controlled: 0 status: public title: Structured prediction by joint kernel support estimation tmp: image: /images/cc_by_nc.png legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) short: CC BY-NC (4.0) type: journal_article volume: 77 year: '2009' ... --- _id: '3690' abstract: - lang: eng text: An important cue to high level scene understanding is to analyze the objects in the scene and their behavior and interactions. In this paper, we study the problem of classification of activities in videos, as this is an integral component of any scene understanding system, and present a novel approach for recognizing human action categories in videos by combining information from appearance and motion of human body parts. Our approach is based on tracking human body parts by using mixture particle filters and then clustering the particles using local non - parametric clustering, hence associating a local set of particles to each cluster mode. The trajectory of these cluster modes provides the "motion" information and the "appearance" information is provided by the statistical information about the relative motion of these local set of particles over a number of frames. Later we use a "Bag of Words" model to build one histogram per video sequence from the set of these robust appearance and motion descriptors. These histograms provide us characteristic information which helps us to discriminate among various human actions which ultimately helps us in better understanding of the complete scene. We tested our approach on the standard KTH and Weizmann human action dataseis and the results were comparable to the state of the art methods. Additionally our approach is able to distinguish between activities that involve the motion of complete body from those in which only certain body parts move. In other words, our method discriminates well between activities with "global body motion" like running, jogging etc. and "local motion" like waving, boxing etc. author: - first_name: Paramveer full_name: Dhillon, Paramveer S last_name: Dhillon - first_name: Sebastian full_name: Nowozin, Sebastian last_name: Nowozin - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Dhillon P, Nowozin S, Lampert C. Combining appearance and motion for human action classification in videos. In: IEEE; 2009:22-29. doi:10.1109/CVPRW.2009.5204237' apa: 'Dhillon, P., Nowozin, S., & Lampert, C. (2009). Combining appearance and motion for human action classification in videos (pp. 22–29). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. https://doi.org/10.1109/CVPRW.2009.5204237' chicago: Dhillon, Paramveer, Sebastian Nowozin, and Christoph Lampert. “Combining Appearance and Motion for Human Action Classification in Videos,” 22–29. IEEE, 2009. https://doi.org/10.1109/CVPRW.2009.5204237. ieee: 'P. Dhillon, S. Nowozin, and C. Lampert, “Combining appearance and motion for human action classification in videos,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, no. 174, pp. 22–29.' ista: 'Dhillon P, Nowozin S, Lampert C. 2009. Combining appearance and motion for human action classification in videos. CVPR: Computer Vision and Pattern Recognition, 22–29.' mla: Dhillon, Paramveer, et al. Combining Appearance and Motion for Human Action Classification in Videos. no. 174, IEEE, 2009, pp. 22–29, doi:10.1109/CVPRW.2009.5204237. short: P. Dhillon, S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 22–29. conference: name: 'CVPR: Computer Vision and Pattern Recognition' date_created: 2018-12-11T12:04:38Z date_published: 2009-01-01T00:00:00Z date_updated: 2021-01-12T07:48:59Z day: '01' doi: 10.1109/CVPRW.2009.5204237 extern: 1 issue: '174' month: '01' page: 22 - 29 publication_status: published publisher: IEEE publist_id: '2675' quality_controlled: 0 status: public title: Combining appearance and motion for human action classification in videos type: conference year: '2009' ... --- _id: '3710' abstract: - lang: eng text: Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object‘s location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the chi^2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition. acknowledgement: 'This work was funded in part by the EU projects CLASS, IST 027978, and PerAct, EST 504321. ' author: - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Matthew full_name: Blaschko,Matthew B last_name: Blaschko - first_name: Thomas full_name: Hofmann,Thomas last_name: Hofmann citation: ama: 'Lampert C, Blaschko M, Hofmann T. Efficient subwindow search: A branch and bound framework for object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009;31(12):2129-2142. doi:10.1109/TPAMI.2009.144' apa: 'Lampert, C., Blaschko, M., & Hofmann, T. (2009). Efficient subwindow search: A branch and bound framework for object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2009.144' chicago: 'Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Efficient Subwindow Search: A Branch and Bound Framework for Object Localization.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2009. https://doi.org/10.1109/TPAMI.2009.144.' ieee: 'C. Lampert, M. Blaschko, and T. Hofmann, “Efficient subwindow search: A branch and bound framework for object localization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12. IEEE, pp. 2129–2142, 2009.' ista: 'Lampert C, Blaschko M, Hofmann T. 2009. Efficient subwindow search: A branch and bound framework for object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(12), 2129–2142.' mla: 'Lampert, Christoph, et al. “Efficient Subwindow Search: A Branch and Bound Framework for Object Localization.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, IEEE, 2009, pp. 2129–42, doi:10.1109/TPAMI.2009.144.' short: C. Lampert, M. Blaschko, T. Hofmann, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2009) 2129–2142. date_created: 2018-12-11T12:04:45Z date_published: 2009-12-01T00:00:00Z date_updated: 2021-01-12T07:51:39Z day: '01' doi: 10.1109/TPAMI.2009.144 extern: 1 intvolume: ' 31' issue: '12' main_file_link: - open_access: '0' url: http://www2.computer.org/portal/web/csdl/doi/10.1109/TPAMI.2009.144 month: '12' page: 2129 - 2142 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_status: published publisher: IEEE publist_id: '2648' quality_controlled: 0 status: public title: 'Efficient subwindow search: A branch and bound framework for object localization' type: journal_article volume: 31 year: '2009' ... --- _id: '3711' abstract: - lang: eng text: An important cue to high level scene understanding is to analyze the objects in the scene and their behavior and interactions. In this paper, we study the problem of classification of activities in videos, as this is an integral component of any scene understanding system, and present a novel approach for recognizing human action categories in videos by combining information from appearance and motion of human body parts. Our approach is based on tracking human body parts by using mixture particle filters and then clustering the particles using local non - parametric clustering, hence associating a local set of particles to each cluster mode. The trajectory of these cluster modes provides the ldquomotionrdquo information and the ldquoappearancerdquo information is provided by the statistical information about the relative motion of these local set of particles over a number of frames. Later we use a ldquoBag of Wordsrdquo model to build one histogram per video sequence from the set of these robust appearance and motion descriptors. These histograms provide us characteristic information which helps us to discriminate among various human actions which ultimately helps us in better understanding of the complete scene. We tested our approach on the standard KTH and Weizmann human action datasets and the results were comparable to the state of the art methods. Additionally our approach is able to distinguish between activities that involve the motion of complete body from those in which only certain body parts move. In other words, our method discriminates well between activities with ldquoglobal body motionrdquo like running, jogging etc. and ldquolocal motionrdquo like waving, boxing etc. author: - first_name: Paramveer full_name: Dhillon, Paramveer S last_name: Dhillon - first_name: Sebastian full_name: Nowozin, Sebastian last_name: Nowozin - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Dhillon P, Nowozin S, Lampert C. Combining appearance and motion for human action classification in videos. In: IEEE; 2009:22-29. doi:10.1109/CVPRW.2009.5204237' apa: 'Dhillon, P., Nowozin, S., & Lampert, C. (2009). Combining appearance and motion for human action classification in videos (pp. 22–29). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. https://doi.org/10.1109/CVPRW.2009.5204237' chicago: Dhillon, Paramveer, Sebastian Nowozin, and Christoph Lampert. “Combining Appearance and Motion for Human Action Classification in Videos,” 22–29. IEEE, 2009. https://doi.org/10.1109/CVPRW.2009.5204237. ieee: 'P. Dhillon, S. Nowozin, and C. Lampert, “Combining appearance and motion for human action classification in videos,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, pp. 22–29.' ista: 'Dhillon P, Nowozin S, Lampert C. 2009. Combining appearance and motion for human action classification in videos. CVPR: Computer Vision and Pattern Recognition, 22–29.' mla: Dhillon, Paramveer, et al. Combining Appearance and Motion for Human Action Classification in Videos. IEEE, 2009, pp. 22–29, doi:10.1109/CVPRW.2009.5204237. short: P. Dhillon, S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 22–29. conference: name: 'CVPR: Computer Vision and Pattern Recognition' date_created: 2018-12-11T12:04:45Z date_published: 2009-08-18T00:00:00Z date_updated: 2021-01-12T07:51:39Z day: '18' doi: 10.1109/CVPRW.2009.5204237 extern: 1 main_file_link: - open_access: '0' url: http://www.nowozin.net/sebastian/papers/dhillon2008actionclassification.pdf month: '08' page: 22 - 29 publication_status: published publisher: IEEE publist_id: '2645' quality_controlled: 0 status: public title: Combining appearance and motion for human action classification in videos type: conference year: '2009' ... --- _id: '3707' abstract: - lang: eng text: Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection and clustering. Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation of statistical dependency and for learning the kernel function itself. All methods are illustrated with examples of successful application from the recent computer vision research literature. alternative_title: - Foundations and Trends® in Computer Graphics and Vision article_processing_charge: No author: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Lampert C. Kernel Methods in Computer Vision. Vol 4. now publishers; 2009. doi:10.1561/0600000027 apa: Lampert, C. (2009). Kernel Methods in Computer Vision (Vol. 4). now publishers. https://doi.org/10.1561/0600000027 chicago: Lampert, Christoph. Kernel Methods in Computer Vision. Vol. 4. now publishers, 2009. https://doi.org/10.1561/0600000027. ieee: C. Lampert, Kernel Methods in Computer Vision, vol. 4. now publishers, 2009. ista: Lampert C. 2009. Kernel Methods in Computer Vision, now publishers, 112p. mla: Lampert, Christoph. Kernel Methods in Computer Vision. Vol. 4, now publishers, 2009, doi:10.1561/0600000027. short: C. Lampert, Kernel Methods in Computer Vision, now publishers, 2009. date_created: 2018-12-11T12:04:44Z date_published: 2009-09-03T00:00:00Z date_updated: 2021-12-21T15:38:43Z day: '03' doi: 10.1561/0600000027 extern: '1' intvolume: ' 4' language: - iso: eng month: '09' oa_version: None page: '112' publication_identifier: eisbn: - 978-1-60198-269-8 isbn: - 978-1-60198-268-1 publication_status: published publisher: now publishers publist_id: '2651' quality_controlled: '1' status: public title: Kernel Methods in Computer Vision type: book user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 volume: 4 year: '2009' ... --- _id: '3708' abstract: - lang: eng text: Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions and cannot model global properties, such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deriving a potential function that enforces the output labeling to be connected and that can naturally be used in the framework of recent MAP-MRF LP relaxations. Using techniques from polyhedral combinatorics, we show that a provably tight approximation to the MAP solution of the resulting MRF can still be found efficiently by solving a sequence of max-flow problems. The efficiency of the inference procedure also allows us to learn the parameters of a MRF with global connectivity potentials by means of a cutting plane algorithm. We experimentally evaluate our algorithm on both synthetic data and on the challenging segmentation task of the PASCAL VOC 2008 data set. We show that in both cases the addition of a connectedness prior significantly reduces the segmentation error. acknowledgement: |- Conference Information URL: http://www.cvpr2009.org/ author: - first_name: Sebastian full_name: Nowozin, Sebastian last_name: Nowozin - first_name: Christoph full_name: Christoph Lampert id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Nowozin S, Lampert C. Global connectivity potentials for random field models. In: IEEE; 2009:818-825. doi:10.1109/CVPR.2009.5206567' apa: 'Nowozin, S., & Lampert, C. (2009). Global connectivity potentials for random field models (pp. 818–825). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. https://doi.org/10.1109/CVPR.2009.5206567' chicago: Nowozin, Sebastian, and Christoph Lampert. “Global Connectivity Potentials for Random Field Models,” 818–25. IEEE, 2009. https://doi.org/10.1109/CVPR.2009.5206567. ieee: 'S. Nowozin and C. Lampert, “Global connectivity potentials for random field models,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, pp. 818–825.' ista: 'Nowozin S, Lampert C. 2009. Global connectivity potentials for random field models. CVPR: Computer Vision and Pattern Recognition, 818–825.' mla: Nowozin, Sebastian, and Christoph Lampert. Global Connectivity Potentials for Random Field Models. IEEE, 2009, pp. 818–25, doi:10.1109/CVPR.2009.5206567. short: S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 818–825. conference: name: 'CVPR: Computer Vision and Pattern Recognition' date_created: 2018-12-11T12:04:44Z date_published: 2009-06-20T00:00:00Z date_updated: 2021-01-12T07:51:38Z day: '20' doi: 10.1109/CVPR.2009.5206567 extern: 1 month: '06' page: 818 - 825 publication_status: published publisher: IEEE publist_id: '2649' quality_controlled: 0 status: public title: Global connectivity potentials for random field models type: conference year: '2009' ...