--- _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' ...