--- _id: '12881' acknowledgement: This work was supported by the DFG (SPP 1527) and the EU (FP7, REA grant no 291734). article_processing_charge: No author: - first_name: Georg S full_name: Martius, Georg S id: 3A276B68-F248-11E8-B48F-1D18A9856A87 last_name: Martius - first_name: Eckehard full_name: Olbrich, Eckehard last_name: Olbrich citation: ama: 'Martius GS, Olbrich E. Quantifying self-organizing behavior of autonomous robots. In: Proceedings of the 13th European Conference on Artificial Life. MIT Press; 2015:78. doi:10.7551/978-0-262-33027-5-ch018' apa: 'Martius, G. S., & Olbrich, E. (2015). Quantifying self-organizing behavior of autonomous robots. In Proceedings of the 13th European Conference on Artificial Life (p. 78). York, United Kingdom: MIT Press. https://doi.org/10.7551/978-0-262-33027-5-ch018' chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.” In Proceedings of the 13th European Conference on Artificial Life, 78. MIT Press, 2015. https://doi.org/10.7551/978-0-262-33027-5-ch018. ieee: G. S. Martius and E. Olbrich, “Quantifying self-organizing behavior of autonomous robots,” in Proceedings of the 13th European Conference on Artificial Life, York, United Kingdom, 2015, p. 78. ista: 'Martius GS, Olbrich E. 2015. Quantifying self-organizing behavior of autonomous robots. Proceedings of the 13th European Conference on Artificial Life. ECAL: European Conference on Artificial Life, 78.' mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.” Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78, doi:10.7551/978-0-262-33027-5-ch018. short: G.S. Martius, E. Olbrich, in:, Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78. conference: end_date: 2015-07-24 location: York, United Kingdom name: 'ECAL: European Conference on Artificial Life' start_date: 2015-07-20 date_created: 2023-04-30T22:01:07Z date_published: 2015-07-01T00:00:00Z date_updated: 2023-05-02T07:06:21Z day: '01' ddc: - '000' department: - _id: ChLa doi: 10.7551/978-0-262-33027-5-ch018 ec_funded: 1 file: - access_level: open_access checksum: 880eabe59c9df12f06a882aa1bc4e600 content_type: application/pdf creator: dernst date_created: 2023-05-02T07:02:59Z date_updated: 2023-05-02T07:02:59Z file_id: '12882' file_name: 2015_ECAL_Martius.pdf file_size: 1674241 relation: main_file success: 1 file_date_updated: 2023-05-02T07:02:59Z has_accepted_license: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '78' project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: Proceedings of the 13th European Conference on Artificial Life publication_identifier: isbn: - '9780262330275' publication_status: published publisher: MIT Press quality_controlled: '1' scopus_import: '1' status: public title: Quantifying self-organizing behavior of autonomous robots 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 user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '1401' abstract: - lang: eng text: 'The human ability to recognize objects in complex scenes has driven research in the computer vision field over couple of decades. This thesis focuses on the object recognition task in images. That is, given the image, we want the computer system to be able to predict the class of the object that appears in the image. A recent successful attempt to bridge semantic understanding of the image perceived by humans and by computers uses attribute-based models. Attributes are semantic properties of the objects shared across different categories, which humans and computers can decide on. To explore the attribute-based models we take a statistical machine learning approach, and address two key learning challenges in view of object recognition task: learning augmented attributes as mid-level discriminative feature representation, and learning with attributes as privileged information. Our main contributions are parametric and non-parametric models and algorithms to solve these frameworks. In the parametric approach, we explore an autoencoder model combined with the large margin nearest neighbor principle for mid-level feature learning, and linear support vector machines for learning with privileged information. In the non-parametric approach, we propose a supervised Indian Buffet Process for automatic augmentation of semantic attributes, and explore the Gaussian Processes classification framework for learning with privileged information. A thorough experimental analysis shows the effectiveness of the proposed models in both parametric and non-parametric views.' acknowledgement: "I would like to thank my supervisor, Christoph Lampert, for guidance throughout my studies and for patience in transforming me into a scientist, and my thesis committee, Chris Wojtan and Horst Bischof, for their help and advice. \r\n\r\nI would like to thank Elisabeth Hacker who perfectly assisted all my administrative needs and was always nice and friendly to me, and the campus team for making the IST Austria campus my second home. \r\nI was honored to collaborate with brilliant researchers and to learn from their experience. Undoubtedly, I learned most of all from Novi Quadrianto: brainstorming our projects and getting exciting results was the most enjoyable part of my work – thank you! I am also grateful to David Knowles, Zoubin Ghahramani, Daniel Hernández-Lobato, Kristian Kersting and Anastasia Pentina for the fantastic projects we worked on together, and to Kristen Grauman and Adriana Kovashka for the exceptional experience working with user studies. I would like to thank my colleagues at IST Austria and my office mates who shared their happy moods, scientific breakthroughs and thought-provoking conversations with me: Chao, Filip, Rustem, Asya, Sameh, Alex, Vlad, Mayu, Neel, Csaba, Thomas, Vladimir, Cristina, Alex Z., Avro, Amelie and Emilie, Andreas H. and Andreas E., Chris, Lena, Michael, Ali and Ipek, Vera, Igor, Katia. Special thanks to Morten for the countless games of table soccer we played together and the tournaments we teamed up for: we will definitely win next time:) A very warm hug to Asya for always being so inspiring and supportive to me, and for helping me to increase the proportion of female computer scientists in our group. " alternative_title: - ISTA Thesis 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 citation: ama: 'Sharmanska V. Learning with attributes for object recognition: Parametric and non-parametrics views. 2015. doi:10.15479/at:ista:1401' apa: 'Sharmanska, V. (2015). Learning with attributes for object recognition: Parametric and non-parametrics views. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:1401' chicago: 'Sharmanska, Viktoriia. “Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views.” Institute of Science and Technology Austria, 2015. https://doi.org/10.15479/at:ista:1401.' ieee: 'V. Sharmanska, “Learning with attributes for object recognition: Parametric and non-parametrics views,” Institute of Science and Technology Austria, 2015.' ista: 'Sharmanska V. 2015. Learning with attributes for object recognition: Parametric and non-parametrics views. Institute of Science and Technology Austria.' mla: 'Sharmanska, Viktoriia. Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views. Institute of Science and Technology Austria, 2015, doi:10.15479/at:ista:1401.' short: 'V. Sharmanska, Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.' date_created: 2018-12-11T11:51:48Z date_published: 2015-04-01T00:00:00Z date_updated: 2023-09-07T11:40:11Z day: '01' ddc: - '000' degree_awarded: PhD department: - _id: ChLa - _id: GradSch doi: 10.15479/at:ista:1401 file: - access_level: open_access checksum: 3605b402bb6934e09ae4cf672c84baf7 content_type: application/pdf creator: dernst date_created: 2021-02-22T11:33:17Z date_updated: 2021-02-22T11:33:17Z file_id: '9177' file_name: 2015_Thesis_Sharmanska.pdf file_size: 7964342 relation: main_file success: 1 - access_level: closed checksum: e37593b3ee75bf3180629df2d6ca8f4e content_type: application/pdf creator: cchlebak date_created: 2021-11-16T14:40:45Z date_updated: 2021-11-17T13:47:24Z file_id: '10297' file_name: 2015_Thesis_Sharmanska_pdfa.pdf file_size: 7372241 relation: main_file file_date_updated: 2021-11-17T13:47:24Z has_accepted_license: '1' language: - iso: eng main_file_link: - url: http://users.sussex.ac.uk/~nq28/viktoriia/Thesis_Sharmanska.pdf month: '04' oa: 1 oa_version: Published Version page: '144' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria publist_id: '5806' status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: 'Learning with attributes for object recognition: Parametric and non-parametrics views' type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2015' ... --- _id: '1655' abstract: - lang: eng text: Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration. acknowledgement: This work was supported by the DFG priority program 1527 (Autonomous Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734. article_processing_charge: No author: - first_name: Georg S full_name: Martius, Georg S id: 3A276B68-F248-11E8-B48F-1D18A9856A87 last_name: Martius - first_name: Eckehard full_name: Olbrich, Eckehard last_name: Olbrich citation: ama: Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots. Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266 apa: Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. Entropy. MDPI. https://doi.org/10.3390/e17107266 chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” Entropy. MDPI, 2015. https://doi.org/10.3390/e17107266. ieee: G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015. ista: Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots. Entropy. 17(10), 7266–7297. mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:10.3390/e17107266. short: G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297. date_created: 2018-12-11T11:53:17Z date_published: 2015-10-23T00:00:00Z date_updated: 2023-10-17T11:42:00Z day: '23' ddc: - '000' department: - _id: ChLa - _id: GaTk doi: 10.3390/e17107266 ec_funded: 1 file: - access_level: open_access checksum: 945d99631a96e0315acb26dc8541dcf9 content_type: application/pdf creator: system date_created: 2018-12-12T10:12:25Z date_updated: 2020-07-14T12:45:08Z file_id: '4943' file_name: IST-2016-464-v1+1_entropy-17-07266.pdf file_size: 6455007 relation: main_file file_date_updated: 2020-07-14T12:45:08Z has_accepted_license: '1' intvolume: ' 17' issue: '10' language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 7266 - 7297 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: Entropy publication_status: published publisher: MDPI publist_id: '5495' pubrep_id: '464' quality_controlled: '1' scopus_import: '1' status: public title: Quantifying emergent behavior of autonomous robots 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: 17 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: '2057' abstract: - lang: eng text: 'In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle this problem, we investigate a recent and elegant well-founded quadratic program named MinCq coming from the machine learning PAC-Bayesian theory. MinCq looks for the weighted combination, over a set of real-valued functions seen as voters, leading to the lowest misclassification rate, while maximizing the voters’ diversity. We propose an extension of MinCq tailored to multimedia indexing. Our method is based on an order-preserving pairwise loss adapted to ranking that allows us to improve Mean Averaged Precision measure while taking into account the diversity of the voters that we want to fuse. We provide evidence that this method is naturally adapted to late fusion procedures and confirm the good behavior of our approach on the challenging PASCAL VOC’07 benchmark.' alternative_title: - LNCS author: - first_name: Emilie full_name: Morvant, Emilie id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87 last_name: Morvant orcid: 0000-0002-8301-7240 - first_name: Amaury full_name: Habrard, Amaury last_name: Habrard - first_name: Stéphane full_name: Ayache, Stéphane last_name: Ayache citation: ama: 'Morvant E, Habrard A, Ayache S. Majority vote of diverse classifiers for late fusion. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8621. Springer; 2014:153-162. doi:10.1007/978-3-662-44415-3_16' apa: 'Morvant, E., Habrard, A., & Ayache, S. (2014). Majority vote of diverse classifiers for late fusion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8621, pp. 153–162). Joensuu, Finland: Springer. https://doi.org/10.1007/978-3-662-44415-3_16' chicago: Morvant, Emilie, Amaury Habrard, and Stéphane Ayache. “Majority Vote of Diverse Classifiers for Late Fusion.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8621:153–62. Springer, 2014. https://doi.org/10.1007/978-3-662-44415-3_16. ieee: E. Morvant, A. Habrard, and S. Ayache, “Majority vote of diverse classifiers for late fusion,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joensuu, Finland, 2014, vol. 8621, pp. 153–162. ista: 'Morvant E, Habrard A, Ayache S. 2014. Majority vote of diverse classifiers for late fusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). IAPR: International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, LNCS, vol. 8621, 153–162.' mla: Morvant, Emilie, et al. “Majority Vote of Diverse Classifiers for Late Fusion.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8621, Springer, 2014, pp. 153–62, doi:10.1007/978-3-662-44415-3_16. short: E. Morvant, A. Habrard, S. Ayache, in:, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 153–162. conference: end_date: 2014-08-22 location: Joensuu, Finland name: 'IAPR: International Workshop on Structural, Syntactic, and Statistical Pattern Recognition' start_date: 2014-08-20 date_created: 2018-12-11T11:55:28Z date_published: 2014-01-01T00:00:00Z date_updated: 2021-01-12T06:55:01Z day: '01' department: - _id: ChLa doi: 10.1007/978-3-662-44415-3_16 ec_funded: 1 external_id: arxiv: - '1404.7796' intvolume: ' 8621' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1404.7796 month: '01' oa: 1 oa_version: Preprint page: 153 - 162 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: '4989' quality_controlled: '1' scopus_import: 1 status: public title: Majority vote of diverse classifiers for late fusion type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 8621 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: '2180' abstract: - lang: eng text: Weighted majority votes allow one to combine the output of several classifiers or voters. MinCq is a recent algorithm for optimizing the weight of each voter based on the minimization of a theoretical bound over the risk of the vote with elegant PAC-Bayesian generalization guarantees. However, while it has demonstrated good performance when combining weak classifiers, MinCq cannot make use of the useful a priori knowledge that one may have when using a mixture of weak and strong voters. In this paper, we propose P-MinCq, an extension of MinCq that can incorporate such knowledge in the form of a constraint over the distribution of the weights, along with general proofs of convergence that stand in the sample compression setting for data-dependent voters. The approach is applied to a vote of k-NN classifiers with a specific modeling of the voters' performance. P-MinCq significantly outperforms the classic k-NN classifier, a symmetric NN and MinCq using the same voters. We show that it is also competitive with LMNN, a popular metric learning algorithm, and that combining both approaches further reduces the error. acknowledgement: 'This work was funded by the French project SoLSTiCe ANR-13-BS02-01 of the ANR. ' author: - first_name: Aurélien full_name: Bellet, Aurélien last_name: Bellet - first_name: Amaury full_name: Habrard, Amaury last_name: Habrard - first_name: Emilie full_name: Morvant, Emilie id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87 last_name: Morvant orcid: 0000-0002-8301-7240 - first_name: Marc full_name: Sebban, Marc last_name: Sebban citation: ama: Bellet A, Habrard A, Morvant E, Sebban M. Learning a priori constrained weighted majority votes. Machine Learning. 2014;97(1-2):129-154. doi:10.1007/s10994-014-5462-z apa: Bellet, A., Habrard, A., Morvant, E., & Sebban, M. (2014). Learning a priori constrained weighted majority votes. Machine Learning. Springer. https://doi.org/10.1007/s10994-014-5462-z chicago: Bellet, Aurélien, Amaury Habrard, Emilie Morvant, and Marc Sebban. “Learning a Priori Constrained Weighted Majority Votes.” Machine Learning. Springer, 2014. https://doi.org/10.1007/s10994-014-5462-z. ieee: A. Bellet, A. Habrard, E. Morvant, and M. Sebban, “Learning a priori constrained weighted majority votes,” Machine Learning, vol. 97, no. 1–2. Springer, pp. 129–154, 2014. ista: Bellet A, Habrard A, Morvant E, Sebban M. 2014. Learning a priori constrained weighted majority votes. Machine Learning. 97(1–2), 129–154. mla: Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.” Machine Learning, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:10.1007/s10994-014-5462-z. short: A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014) 129–154. date_created: 2018-12-11T11:56:10Z date_published: 2014-10-01T00:00:00Z date_updated: 2021-01-12T06:55:49Z day: '01' department: - _id: ChLa doi: 10.1007/s10994-014-5462-z ec_funded: 1 intvolume: ' 97' issue: 1-2 language: - iso: eng main_file_link: - open_access: '1' url: https://hal.archives-ouvertes.fr/hal-01009578/document month: '10' oa: 1 oa_version: Submitted Version page: 129 - 154 project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication: Machine Learning publication_status: published publisher: Springer publist_id: '4802' quality_controlled: '1' scopus_import: 1 status: public title: Learning a priori constrained weighted majority votes type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 97 year: '2014' ... --- _id: '2189' abstract: - lang: fre text: En apprentissage automatique, nous parlons d'adaptation de domaine lorsque les données de test (cibles) et d'apprentissage (sources) sont générées selon différentes distributions. Nous devons donc développer des algorithmes de classification capables de s'adapter à une nouvelle distribution, pour laquelle aucune information sur les étiquettes n'est disponible. Nous attaquons cette problématique sous l'angle de l'approche PAC-Bayésienne qui se focalise sur l'apprentissage de modèles définis comme des votes de majorité sur un ensemble de fonctions. Dans ce contexte, nous introduisons PV-MinCq une version adaptative de l'algorithme (non adaptatif) MinCq. PV-MinCq suit le principe suivant. Nous transférons les étiquettes sources aux points cibles proches pour ensuite appliquer MinCq sur l'échantillon cible ``auto-étiqueté'' (justifié par une borne théorique). Plus précisément, nous définissons un auto-étiquetage non itératif qui se focalise dans les régions où les distributions marginales source et cible sont les plus similaires. Dans un second temps, nous étudions l'influence de notre auto-étiquetage pour en déduire une procédure de validation des hyperparamètres. Finalement, notre approche montre des résultats empiriques prometteurs. article_processing_charge: No author: - first_name: Emilie full_name: Morvant, Emilie id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87 last_name: Morvant orcid: 0000-0002-8301-7240 citation: ama: 'Morvant E. Adaptation de domaine de vote de majorité par auto-étiquetage non itératif. In: Vol 1. Elsevier; 2014:49-58.' apa: 'Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne, France: Elsevier.' chicago: Morvant, Emilie. “Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage Non Itératif,” 1:49–58. Elsevier, 2014. ieee: 'E. Morvant, “Adaptation de domaine de vote de majorité par auto-étiquetage non itératif,” presented at the CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne, France, 2014, vol. 1, pp. 49–58.' ista: 'Morvant E. 2014. Adaptation de domaine de vote de majorité par auto-étiquetage non itératif. CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference) vol. 1, 49–58.' mla: Morvant, Emilie. Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage Non Itératif. Vol. 1, Elsevier, 2014, pp. 49–58. short: E. Morvant, in:, Elsevier, 2014, pp. 49–58. conference: location: Saint-Etienne, France name: 'CAP: Conférence Francophone sur l''Apprentissage Automatique (Machine Learning French Conference)' date_created: 2018-12-11T11:56:13Z date_published: 2014-07-01T00:00:00Z date_updated: 2021-01-12T06:55:52Z day: '01' department: - _id: ChLa intvolume: ' 1' language: - iso: eng main_file_link: - open_access: '1' url: https://hal.archives-ouvertes.fr/hal-01005776/ month: '07' oa: 1 oa_version: Preprint page: 49-58 publication_status: published publisher: Elsevier publist_id: '4785' quality_controlled: '1' status: public title: Adaptation de domaine de vote de majorité par auto-étiquetage non itératif type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 1 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: '2520' abstract: - lang: eng text: "We propose a probabilistic model to infer supervised latent variables in\r\nthe Hamming space from observed data. Our model allows simultaneous\r\ninference of the number of binary latent variables, and their values. The\r\nlatent variables preserve neighbourhood structure of the data in a sense\r\nthat objects in the same semantic concept have similar latent values, and\r\nobjects in different concepts have dissimilar latent values. We formulate\r\nthe supervised infinite latent variable problem based on an intuitive\r\nprinciple of pulling objects together if they are of the same type, and\r\npushing them apart if they are not. We then combine this principle with a\r\nflexible Indian Buffet Process prior on the latent variables. We show that\r\nthe inferred supervised latent variables can be directly used to perform a\r\nnearest neighbour search for the purpose of retrieval. We introduce a new\r\napplication of dynamically extending hash codes, and show how to\r\neffectively couple the structure of the hash codes with continuously\r\ngrowing structure of the neighbourhood preserving infinite latent feature\r\nspace." author: - first_name: Novi full_name: Quadrianto, Novi last_name: Quadrianto - first_name: Viktoriia full_name: Sharmanska, Viktoriia id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87 last_name: Sharmanska orcid: 0000-0003-0192-9308 - first_name: David full_name: Knowles, David last_name: Knowles - first_name: Zoubin full_name: Ghahramani, Zoubin last_name: Ghahramani citation: ama: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. The supervised IBP: Neighbourhood preserving infinite latent feature models. In: Proceedings of the 29th Conference Uncertainty in Artificial Intelligence. AUAI Press; 2013:527-536.' apa: 'Quadrianto, N., Sharmanska, V., Knowles, D., & Ghahramani, Z. (2013). The supervised IBP: Neighbourhood preserving infinite latent feature models. In Proceedings of the 29th conference uncertainty in Artificial Intelligence (pp. 527–536). Bellevue, WA, United States: AUAI Press.' chicago: 'Quadrianto, Novi, Viktoriia Sharmanska, David Knowles, and Zoubin Ghahramani. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” In Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, 527–36. AUAI Press, 2013.' ieee: 'N. Quadrianto, V. Sharmanska, D. Knowles, and Z. Ghahramani, “The supervised IBP: Neighbourhood preserving infinite latent feature models,” in Proceedings of the 29th conference uncertainty in Artificial Intelligence, Bellevue, WA, United States, 2013, pp. 527–536.' ista: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. 2013. The supervised IBP: Neighbourhood preserving infinite latent feature models. Proceedings of the 29th conference uncertainty in Artificial Intelligence. UAI: Uncertainty in Artificial Intelligence, 527–536.' mla: 'Quadrianto, Novi, et al. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013, pp. 527–36.' short: N. Quadrianto, V. Sharmanska, D. Knowles, Z. Ghahramani, in:, Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013, pp. 527–536. conference: end_date: 2013-07-15 location: Bellevue, WA, United States name: 'UAI: Uncertainty in Artificial Intelligence' start_date: 2013-07-11 date_created: 2018-12-11T11:58:09Z date_published: 2013-07-11T00:00:00Z date_updated: 2023-02-23T10:46:36Z day: '11' ddc: - '000' department: - _id: ChLa file: - access_level: open_access checksum: 325f20c4b926bd74d39006b97df572bd content_type: application/pdf creator: system date_created: 2018-12-12T10:15:16Z date_updated: 2020-07-14T12:45:42Z file_id: '5134' file_name: IST-2013-137-v1+1_QuaShaKnoGha13.pdf file_size: 1117100 relation: main_file file_date_updated: 2020-07-14T12:45:42Z has_accepted_license: '1' language: - iso: eng month: '07' oa: 1 oa_version: Submitted Version page: 527 - 536 publication: Proceedings of the 29th conference uncertainty in Artificial Intelligence publication_identifier: isbn: - '9780974903996' publication_status: published publisher: AUAI Press publist_id: '4381' pubrep_id: '137' quality_controlled: '1' scopus_import: 1 status: public title: 'The supervised IBP: Neighbourhood preserving infinite latent feature models' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 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: '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: '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' ...