--- _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 license: https://creativecommons.org/licenses/by/4.0/ 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' ...