[{"language":[{"iso":"eng"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"conference":{"end_date":"2020-07-18","start_date":"2020-07-12","location":"Online","name":"ICML: International Conference on Machine Learning"},"project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020"}],"quality_controlled":"1","oa":1,"external_id":{"arxiv":["2002.10384"]},"publication_identifier":{"issn":["2640-3498"]},"month":"07","volume":119,"date_created":"2020-11-05T15:25:58Z","date_updated":"2023-09-07T13:42:08Z","related_material":{"record":[{"id":"10799","relation":"dissertation_contains","status":"public"}],"link":[{"url":"http://proceedings.mlr.press/v119/konstantinov20a/konstantinov20a-supp.pdf","relation":"supplementary_material"}]},"author":[{"full_name":"Konstantinov, Nikola H","first_name":"Nikola H","last_name":"Konstantinov","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Frantar, Elias","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","last_name":"Frantar","first_name":"Elias"},{"full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","first_name":"Dan-Adrian"},{"orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph"}],"publisher":"ML Research Press","department":[{"_id":"DaAl"},{"_id":"ChLa"}],"publication_status":"published","year":"2020","acknowledgement":"Dan Alistarh is supported in part by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).","ec_funded":1,"file_date_updated":"2021-02-15T09:00:01Z","date_published":"2020-07-12T00:00:00Z","page":"5416-5425","citation":{"chicago":"Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.","short":"N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.","mla":"Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.","ieee":"N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.","apa":"Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.","ista":"Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample complexity of adversarial multi-source PAC learning. Proceedings of the 37th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 119, 5416–5425.","ama":"Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425."},"publication":"Proceedings of the 37th International Conference on Machine Learning","has_accepted_license":"1","article_processing_charge":"No","day":"12","scopus_import":"1","oa_version":"Published Version","file":[{"file_id":"9120","relation":"main_file","date_created":"2021-02-15T09:00:01Z","date_updated":"2021-02-15T09:00:01Z","success":1,"checksum":"cc755d0054bc4b2be778ea7aa7884d2f","file_name":"2020_PMLR_Konstantinov.pdf","access_level":"open_access","creator":"dernst","content_type":"application/pdf","file_size":281286}],"intvolume":" 119","ddc":["000"],"title":"On the sample complexity of adversarial multi-source PAC learning","status":"public","_id":"8724","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms. Specifically, we analyze the situation in which a learning system obtains datasets from multiple sources, some of which might be biased or even adversarially perturbed. It is\r\nknown that in the single-source case, an adversary with the power to corrupt a fixed fraction of the training data can prevent PAC-learnability, that is, even in the limit of infinitely much training data, no learning system can approach the optimal test error. In this work we show that, surprisingly, the same is not true in the multi-source setting, where the adversary can arbitrarily\r\ncorrupt a fixed fraction of the data sources. Our main results are a generalization bound that provides finite-sample guarantees for this learning setting, as well as corresponding lower bounds. Besides establishing PAC-learnability our results also show that in a cooperative learning setting sharing data with other parties has provable benefits, even if some\r\nparticipants are malicious. ","lang":"eng"}],"type":"conference"},{"date_published":"2020-09-14T00:00:00Z","citation":{"chicago":"Royer, Amélie. “Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8390.","mla":"Royer, Amélie. Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8390.","short":"A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models, Institute of Science and Technology Austria, 2020.","ista":"Royer A. 2020. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. Institute of Science and Technology Austria.","apa":"Royer, A. (2020). Leveraging structure in Computer Vision tasks for flexible Deep Learning models. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8390","ieee":"A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020.","ama":"Royer A. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. 2020. doi:10.15479/AT:ISTA:8390"},"page":"197","day":"14","has_accepted_license":"1","article_processing_charge":"No","oa_version":"Published Version","file":[{"file_id":"8391","relation":"main_file","date_updated":"2020-09-14T13:39:14Z","date_created":"2020-09-14T13:39:14Z","success":1,"checksum":"c914d2f88846032f3d8507734861b6ee","file_name":"2020_Thesis_Royer.pdf","access_level":"open_access","creator":"dernst","file_size":30224591,"content_type":"application/pdf"},{"checksum":"ae98fb35d912cff84a89035ae5794d3c","date_updated":"2020-09-14T13:39:17Z","date_created":"2020-09-14T13:39:17Z","relation":"main_file","file_id":"8392","content_type":"application/x-zip-compressed","file_size":74227627,"creator":"dernst","access_level":"closed","file_name":"thesis_sources.zip"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"8390","title":"Leveraging structure in Computer Vision tasks for flexible Deep Learning models","status":"public","ddc":["000"],"abstract":[{"lang":"eng","text":"Deep neural networks have established a new standard for data-dependent feature extraction pipelines in the Computer Vision literature. Despite their remarkable performance in the standard supervised learning scenario, i.e. when models are trained with labeled data and tested on samples that follow a similar distribution, neural networks have been shown to struggle with more advanced generalization abilities, such as transferring knowledge across visually different domains, or generalizing to new unseen combinations of known concepts. In this thesis we argue that, in contrast to the usual black-box behavior of neural networks, leveraging more structured internal representations is a promising direction\r\nfor tackling such problems. In particular, we focus on two forms of structure. First, we tackle modularity: We show that (i) compositional architectures are a natural tool for modeling reasoning tasks, in that they efficiently capture their combinatorial nature, which is key for generalizing beyond the compositions seen during training. We investigate how to to learn such models, both formally and experimentally, for the task of abstract visual reasoning. Then, we show that (ii) in some settings, modularity allows us to efficiently break down complex tasks into smaller, easier, modules, thereby improving computational efficiency; We study this behavior in the context of generative models for colorization, as well as for small objects detection. Secondly, we investigate the inherently layered structure of representations learned by neural networks, and analyze its role in the context of transfer learning and domain adaptation across visually\r\ndissimilar domains. "}],"type":"dissertation","alternative_title":["ISTA Thesis"],"doi":"10.15479/AT:ISTA:8390","acknowledged_ssus":[{"_id":"CampIT"},{"_id":"ScienComp"}],"supervisor":[{"full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","first_name":"Christoph","last_name":"Lampert"}],"degree_awarded":"PhD","language":[{"iso":"eng"}],"oa":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","image":"/images/cc_by_nc_sa.png","short":"CC BY-NC-SA (4.0)"},"month":"09","publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-007-7"]},"author":[{"orcid":"0000-0002-8407-0705","id":"3811D890-F248-11E8-B48F-1D18A9856A87","last_name":"Royer","first_name":"Amélie","full_name":"Royer, Amélie"}],"related_material":{"record":[{"id":"7936","relation":"part_of_dissertation","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"7937"},{"relation":"part_of_dissertation","status":"public","id":"8193"},{"id":"8092","relation":"part_of_dissertation","status":"public"},{"id":"911","status":"public","relation":"part_of_dissertation"}]},"date_updated":"2023-10-16T10:04:02Z","date_created":"2020-09-14T13:42:09Z","year":"2020","acknowledgement":"Last but not least, I would like to acknowledge the support of the IST IT and scientific computing team for helping provide a great work environment.","publication_status":"published","department":[{"_id":"ChLa"}],"publisher":"Institute of Science and Technology Austria","file_date_updated":"2020-09-14T13:39:17Z","license":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},{"date_updated":"2023-10-17T07:37:11Z","date_created":"2020-07-31T16:53:49Z","author":[{"first_name":"Paul M","last_name":"Henderson","id":"13C09E74-18D9-11E9-8878-32CFE5697425","orcid":"0000-0002-5198-7445","full_name":"Henderson, Paul M"},{"full_name":"Tsiminaki, Vagia","first_name":"Vagia","last_name":"Tsiminaki"},{"full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"publisher":"IEEE","department":[{"_id":"ChLa"}],"publication_status":"published","year":"2020","file_date_updated":"2020-07-31T16:57:12Z","language":[{"iso":"eng"}],"doi":"10.1109/CVPR42600.2020.00752","conference":{"name":"CVPR: Conference on Computer Vision and Pattern Recognition","end_date":"2020-06-19","start_date":"2020-06-14","location":"Virtual"},"quality_controlled":"1","main_file_link":[{"url":"https://openaccess.thecvf.com/content_CVPR_2020/papers/Henderson_Leveraging_2D_Data_to_Learn_Textured_3D_Mesh_Generation_CVPR_2020_paper.pdf","open_access":"1"}],"oa":1,"external_id":{"arxiv":["2004.04180"]},"publication_identifier":{"eissn":["2575-7075"],"eisbn":["9781728171685"]},"month":"07","file":[{"access_level":"open_access","file_name":"paper.pdf","file_size":10262773,"content_type":"application/pdf","creator":"phenders","relation":"main_file","file_id":"8187","success":1,"date_updated":"2020-07-31T16:57:12Z","date_created":"2020-07-31T16:57:12Z"}],"oa_version":"Submitted Version","status":"public","title":"Leveraging 2D data to learn textured 3D mesh generation","ddc":["004"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"8186","abstract":[{"lang":"eng","text":"Numerous methods have been proposed for probabilistic generative modelling of\r\n3D objects. However, none of these is able to produce textured objects, which\r\nrenders them of limited use for practical tasks. In this work, we present the\r\nfirst generative model of textured 3D meshes. Training such a model would\r\ntraditionally require a large dataset of textured meshes, but unfortunately,\r\nexisting datasets of meshes lack detailed textures. We instead propose a new\r\ntraining methodology that allows learning from collections of 2D images without\r\nany 3D information. To do so, we train our model to explain a distribution of\r\nimages by modelling each image as a 3D foreground object placed in front of a\r\n2D background. Thus, it learns to generate meshes that when rendered, produce\r\nimages similar to those in its training set.\r\n A well-known problem when generating meshes with deep networks is the\r\nemergence of self-intersections, which are problematic for many use-cases. As a\r\nsecond contribution we therefore introduce a new generation process for 3D\r\nmeshes that guarantees no self-intersections arise, based on the physical\r\nintuition that faces should push one another out of the way as they move.\r\n We conduct extensive experiments on our approach, reporting quantitative and\r\nqualitative results on both synthetic data and natural images. These show our\r\nmethod successfully learns to generate plausible and diverse textured 3D\r\nsamples for five challenging object classes."}],"type":"conference","date_published":"2020-07-01T00:00:00Z","page":"7498-7507","citation":{"chicago":"Henderson, Paul M, Vagia Tsiminaki, and Christoph Lampert. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7498–7507. IEEE, 2020. https://doi.org/10.1109/CVPR42600.2020.00752.","short":"P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507.","mla":"Henderson, Paul M., et al. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–507, doi:10.1109/CVPR42600.2020.00752.","apa":"Henderson, P. M., Tsiminaki, V., & Lampert, C. (2020). Leveraging 2D data to learn textured 3D mesh generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7498–7507). Virtual: IEEE. https://doi.org/10.1109/CVPR42600.2020.00752","ieee":"P. M. Henderson, V. Tsiminaki, and C. Lampert, “Leveraging 2D data to learn textured 3D mesh generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, 2020, pp. 7498–7507.","ista":"Henderson PM, Tsiminaki V, Lampert C. 2020. Leveraging 2D data to learn textured 3D mesh generation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 7498–7507.","ama":"Henderson PM, Tsiminaki V, Lampert C. Leveraging 2D data to learn textured 3D mesh generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2020:7498-7507. doi:10.1109/CVPR42600.2020.00752"},"publication":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","article_processing_charge":"No","has_accepted_license":"1","day":"01","scopus_import":"1"},{"publication_status":"published","publisher":"Springer Nature","department":[{"_id":"ChLa"}],"year":"2020","date_created":"2019-10-14T09:14:28Z","date_updated":"2024-02-22T14:57:30Z","volume":128,"author":[{"full_name":"Sun, Rémy","last_name":"Sun","first_name":"Rémy"},{"full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","first_name":"Christoph","last_name":"Lampert"}],"related_material":{"link":[{"relation":"erratum","url":"https://doi.org/10.1007/s11263-019-01262-5"}],"record":[{"id":"6482","status":"public","relation":"earlier_version"}]},"license":"https://creativecommons.org/licenses/by/4.0/","file_date_updated":"2020-07-14T12:47:45Z","ec_funded":1,"quality_controlled":"1","isi":1,"project":[{"name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7","grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425"},{"name":"IST Austria Open Access Fund","_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854"}],"oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"isi":["000494406800001"]},"language":[{"iso":"eng"}],"doi":"10.1007/s11263-019-01232-x","month":"04","publication_identifier":{"issn":["0920-5691"],"eissn":["1573-1405"]},"ddc":["004"],"title":"KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications","status":"public","intvolume":" 128","_id":"6944","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","file":[{"relation":"main_file","file_id":"7110","date_updated":"2020-07-14T12:47:45Z","date_created":"2019-11-26T10:30:02Z","checksum":"155e63edf664dcacb3bdc1c2223e606f","file_name":"2019_IJCV_Sun.pdf","access_level":"open_access","content_type":"application/pdf","file_size":1715072,"creator":"dernst"}],"type":"journal_article","abstract":[{"lang":"eng","text":"We study the problem of automatically detecting if a given multi-class classifier operates outside of its specifications (out-of-specs), i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier’s predictions cannot be guaranteed if it operates out-of-specs. Previously proposed methods for out-of-specs detection make decisions on the level of single inputs. This, however, is insufficient to achieve low false positive rate and high false negative rates at the same time. In this work, we describe a new procedure named KS(conf), based on statistical reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied to the set of predicted confidence values for batches of samples. Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change."}],"issue":"4","article_type":"original","page":"970-995","publication":"International Journal of Computer Vision","citation":{"ista":"Sun R, Lampert C. 2020. KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. 128(4), 970–995.","apa":"Sun, R., & Lampert, C. (2020). KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01232-x","ieee":"R. Sun and C. Lampert, “KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications,” International Journal of Computer Vision, vol. 128, no. 4. Springer Nature, pp. 970–995, 2020.","ama":"Sun R, Lampert C. KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. 2020;128(4):970-995. doi:10.1007/s11263-019-01232-x","chicago":"Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass Classifier Operates Outside of Its Specifications.” International Journal of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01232-x.","mla":"Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass Classifier Operates Outside of Its Specifications.” International Journal of Computer Vision, vol. 128, no. 4, Springer Nature, 2020, pp. 970–95, doi:10.1007/s11263-019-01232-x.","short":"R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995."},"date_published":"2020-04-01T00:00:00Z","scopus_import":"1","day":"01","has_accepted_license":"1","article_processing_charge":"Yes (via OA deal)"},{"month":"10","day":"30","publication_identifier":{"isbn":["978-3-658-26762-9"],"eisbn":["978-3-658-26763-6"]},"article_processing_charge":"No","quality_controlled":"1","page":"XIV, 245","citation":{"chicago":"Kersting, Kristian, Christoph Lampert, and Constantin Rothkopf, eds. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden: Springer Nature, 2019. https://doi.org/10.1007/978-3-658-26763-6.","mla":"Kersting, Kristian, et al., editors. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed., Springer Nature, 2019, doi:10.1007/978-3-658-26763-6.","short":"K. Kersting, C. Lampert, C. Rothkopf, eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed., Springer Nature, Wiesbaden, 2019.","ista":"Kersting K, Lampert C, Rothkopf C eds. 2019. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt 1st ed., Wiesbaden: Springer Nature, XIV, 245p.","apa":"Kersting, K., Lampert, C., & Rothkopf, C. (Eds.). (2019). Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt (1st ed.). Wiesbaden: Springer Nature. https://doi.org/10.1007/978-3-658-26763-6","ieee":"K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature, 2019.","ama":"Kersting K, Lampert C, Rothkopf C, eds. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden: Springer Nature; 2019. doi:10.1007/978-3-658-26763-6"},"language":[{"iso":"ger"}],"doi":"10.1007/978-3-658-26763-6","date_published":"2019-10-30T00:00:00Z","place":"Wiesbaden","type":"book_editor","abstract":[{"lang":"ger","text":"Wissen Sie, was sich hinter künstlicher Intelligenz und maschinellem Lernen verbirgt? \r\nDieses Sachbuch erklärt Ihnen leicht verständlich und ohne komplizierte Formeln die grundlegenden Methoden und Vorgehensweisen des maschinellen Lernens. Mathematisches Vorwissen ist dafür nicht nötig. Kurzweilig und informativ illustriert Lisa, die Protagonistin des Buches, diese anhand von Alltagssituationen. \r\nEin Buch für alle, die in Diskussionen über Chancen und Risiken der aktuellen Entwicklung der künstlichen Intelligenz und des maschinellen Lernens mit Faktenwissen punkten möchten. Auch für Schülerinnen und Schüler geeignet!"}],"status":"public","title":"Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt","publication_status":"published","department":[{"_id":"ChLa"}],"editor":[{"first_name":"Kristian","last_name":"Kersting","full_name":"Kersting, Kristian"},{"full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph"},{"last_name":"Rothkopf","first_name":"Constantin","full_name":"Rothkopf, Constantin"}],"publisher":"Springer Nature","_id":"7171","year":"2019","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_created":"2019-12-11T14:15:56Z","date_updated":"2021-12-22T14:40:58Z","oa_version":"None","related_material":{"link":[{"url":"https://ist.ac.at/en/news/book-release-how-machines-learn/","description":"News on IST Website","relation":"press_release"}]},"edition":"1"},{"year":"2019","publisher":"Springer Nature","department":[{"_id":"KrCh"},{"_id":"ChLa"}],"publication_status":"published","author":[{"full_name":"Ashok, Pranav","first_name":"Pranav","last_name":"Ashok"},{"full_name":"Brázdil, Tomáš","last_name":"Brázdil","first_name":"Tomáš"},{"full_name":"Chatterjee, Krishnendu","first_name":"Krishnendu","last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X"},{"last_name":"Křetínský","first_name":"Jan","full_name":"Křetínský, Jan"},{"full_name":"Lampert, Christoph","last_name":"Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Toman, Viktor","last_name":"Toman","first_name":"Viktor","orcid":"0000-0001-9036-063X","id":"3AF3DA7C-F248-11E8-B48F-1D18A9856A87"}],"volume":11785,"date_updated":"2023-08-30T06:59:36Z","date_created":"2019-10-14T06:57:49Z","publication_identifier":{"eisbn":["9783030302818"],"issn":["0302-9743"],"isbn":["9783030302801"]},"month":"09","oa":1,"external_id":{"arxiv":["1906.08178"],"isi":["000679281300007"]},"main_file_link":[{"url":"https://arxiv.org/abs/1906.08178","open_access":"1"}],"project":[{"_id":"25863FF4-B435-11E9-9278-68D0E5697425","grant_number":"S11407","call_identifier":"FWF","name":"Game Theory"},{"grant_number":"S11402-N23","_id":"25F2ACDE-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Rigorous Systems Engineering"},{"grant_number":"ICT15-003","_id":"25892FC0-B435-11E9-9278-68D0E5697425","name":"Efficient Algorithms for Computer Aided Verification"}],"isi":1,"quality_controlled":"1","doi":"10.1007/978-3-030-30281-8_7","conference":{"name":"QEST: Quantitative Evaluation of Systems","start_date":"2019-09-10","location":"Glasgow, United Kingdom","end_date":"2019-09-12"},"language":[{"iso":"eng"}],"type":"conference","alternative_title":["LNCS"],"abstract":[{"text":"Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of 𝜔 -regular winning conditions; e.g., safety, reachability, liveness, parity conditions; provides a robust and expressive specification formalism for properties that arise in analysis of reactive systems. The resolutions of nondeterminism in games and MDPs are represented as strategies, and we consider succinct representation of such strategies. The decision-tree data structure from machine learning retains the flavor of decisions of strategies and allows entropy-based minimization to obtain succinct trees. However, in contrast to traditional machine-learning problems where small errors are allowed, for winning strategies in graph games and MDPs no error is allowed, and the decision tree must represent the entire strategy. In this work we propose decision trees with linear classifiers for representation of strategies in graph games and MDPs. We have implemented strategy representation using this data structure and we present experimental results for problems on graph games and MDPs, which show that this new data structure presents a much more efficient strategy representation as compared to standard decision trees.","lang":"eng"}],"_id":"6942","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","intvolume":" 11785","title":"Strategy representation by decision trees with linear classifiers","status":"public","oa_version":"Preprint","scopus_import":"1","article_processing_charge":"No","day":"04","citation":{"ieee":"P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, and V. Toman, “Strategy representation by decision trees with linear classifiers,” in 16th International Conference on Quantitative Evaluation of Systems, Glasgow, United Kingdom, 2019, vol. 11785, pp. 109–128.","apa":"Ashok, P., Brázdil, T., Chatterjee, K., Křetínský, J., Lampert, C., & Toman, V. (2019). Strategy representation by decision trees with linear classifiers. In 16th International Conference on Quantitative Evaluation of Systems (Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature. https://doi.org/10.1007/978-3-030-30281-8_7","ista":"Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. 2019. Strategy representation by decision trees with linear classifiers. 16th International Conference on Quantitative Evaluation of Systems. QEST: Quantitative Evaluation of Systems, LNCS, vol. 11785, 109–128.","ama":"Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. Strategy representation by decision trees with linear classifiers. In: 16th International Conference on Quantitative Evaluation of Systems. Vol 11785. Springer Nature; 2019:109-128. doi:10.1007/978-3-030-30281-8_7","chicago":"Ashok, Pranav, Tomáš Brázdil, Krishnendu Chatterjee, Jan Křetínský, Christoph Lampert, and Viktor Toman. “Strategy Representation by Decision Trees with Linear Classifiers.” In 16th International Conference on Quantitative Evaluation of Systems, 11785:109–28. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-30281-8_7.","short":"P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, V. Toman, in:, 16th International Conference on Quantitative Evaluation of Systems, Springer Nature, 2019, pp. 109–128.","mla":"Ashok, Pranav, et al. “Strategy Representation by Decision Trees with Linear Classifiers.” 16th International Conference on Quantitative Evaluation of Systems, vol. 11785, Springer Nature, 2019, pp. 109–28, doi:10.1007/978-3-030-30281-8_7."},"publication":"16th International Conference on Quantitative Evaluation of Systems","page":"109-128","date_published":"2019-09-04T00:00:00Z"},{"abstract":[{"text":"Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.","lang":"eng"}],"issue":"9","type":"journal_article","oa_version":"Preprint","title":"Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly","status":"public","intvolume":" 41","_id":"6554","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","day":"01","article_processing_charge":"No","scopus_import":"1","date_published":"2019-09-01T00:00:00Z","article_type":"original","page":"2251 - 2265","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","citation":{"chicago":"Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE), 2019. https://doi.org/10.1109/tpami.2018.2857768.","mla":"Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9, Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 2251–65, doi:10.1109/tpami.2018.2857768.","short":"Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2019) 2251–2265.","ista":"Xian Y, Lampert C, Schiele B, Akata Z. 2019. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(9), 2251–2265.","ieee":"Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9. Institute of Electrical and Electronics Engineers (IEEE), pp. 2251–2265, 2019.","apa":"Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2019). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tpami.2018.2857768","ama":"Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2019;41(9):2251-2265. doi:10.1109/tpami.2018.2857768"},"date_created":"2019-06-11T14:05:59Z","date_updated":"2023-09-05T13:18:09Z","volume":41,"author":[{"full_name":"Xian, Yongqin","last_name":"Xian","first_name":"Yongqin"},{"full_name":"Lampert, Christoph","orcid":"0000-0002-4561-241X","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph"},{"last_name":"Schiele","first_name":"Bernt","full_name":"Schiele, Bernt"},{"full_name":"Akata, Zeynep","first_name":"Zeynep","last_name":"Akata"}],"publication_status":"published","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","department":[{"_id":"ChLa"}],"year":"2019","month":"09","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"language":[{"iso":"eng"}],"doi":"10.1109/tpami.2018.2857768","isi":1,"quality_controlled":"1","external_id":{"arxiv":["1707.00600"],"isi":["000480343900015"]},"main_file_link":[{"url":"https://arxiv.org/abs/1707.00600","open_access":"1"}],"oa":1},{"type":"conference","abstract":[{"text":"Multi-exit architectures, in which a stack of processing layers is interleaved with early output layers, allow the processing of a test example to stop early and thus save computation time and/or energy. In this work, we propose a new training procedure for multi-exit architectures based on the principle of knowledge distillation. The method encourage searly exits to mimic later, more accurate exits, by matching their output probabilities.\r\nExperiments on CIFAR100 and ImageNet show that distillation-based training significantly improves the accuracy of early exits while maintaining state-of-the-art accuracy for late ones. The method is particularly beneficial when training data is limited and it allows a straightforward extension to semi-supervised learning,i.e. making use of unlabeled data at training time. Moreover, it takes only afew lines to implement and incurs almost no computational overhead at training time, and none at all at test time.","lang":"eng"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"7479","title":"Distillation-based training for multi-exit architectures","status":"public","ddc":["000"],"file":[{"relation":"main_file","file_id":"7480","date_updated":"2020-07-14T12:47:59Z","date_created":"2020-02-11T09:06:39Z","checksum":"7b77fb5c2d27c4c37a7612ba46a66117","file_name":"main.pdf","access_level":"open_access","file_size":735768,"content_type":"application/pdf","creator":"bphuong"}],"oa_version":"Submitted Version","scopus_import":"1","article_processing_charge":"No","has_accepted_license":"1","day":"01","citation":{"ama":"Phuong M, Lampert C. Distillation-based training for multi-exit architectures. In: IEEE International Conference on Computer Vision. Vol 2019-October. IEEE; 2019:1355-1364. doi:10.1109/ICCV.2019.00144","ieee":"M. Phuong and C. Lampert, “Distillation-based training for multi-exit architectures,” in IEEE International Conference on Computer Vision, Seoul, Korea, 2019, vol. 2019–October, pp. 1355–1364.","apa":"Phuong, M., & Lampert, C. (2019). Distillation-based training for multi-exit architectures. In IEEE International Conference on Computer Vision (Vol. 2019–October, pp. 1355–1364). Seoul, Korea: IEEE. https://doi.org/10.1109/ICCV.2019.00144","ista":"Phuong M, Lampert C. 2019. Distillation-based training for multi-exit architectures. IEEE International Conference on Computer Vision. ICCV: International Conference on Computer Vision vol. 2019–October, 1355–1364.","short":"M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision, IEEE, 2019, pp. 1355–1364.","mla":"Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit Architectures.” IEEE International Conference on Computer Vision, vol. 2019–October, IEEE, 2019, pp. 1355–64, doi:10.1109/ICCV.2019.00144.","chicago":"Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit Architectures.” In IEEE International Conference on Computer Vision, 2019–October:1355–64. IEEE, 2019. https://doi.org/10.1109/ICCV.2019.00144."},"publication":"IEEE International Conference on Computer Vision","page":"1355-1364","date_published":"2019-10-01T00:00:00Z","ec_funded":1,"file_date_updated":"2020-07-14T12:47:59Z","year":"2019","publisher":"IEEE","department":[{"_id":"ChLa"}],"publication_status":"published","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"9418"}]},"author":[{"id":"3EC6EE64-F248-11E8-B48F-1D18A9856A87","first_name":"Phuong","last_name":"Bui Thi Mai","full_name":"Bui Thi Mai, Phuong"},{"last_name":"Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"volume":"2019-October","date_created":"2020-02-11T09:06:57Z","date_updated":"2023-09-08T11:11:12Z","publication_identifier":{"isbn":["9781728148038"],"issn":["15505499"]},"month":"10","external_id":{"isi":["000531438101047"]},"oa":1,"project":[{"name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7","grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","isi":1,"doi":"10.1109/ICCV.2019.00144","conference":{"name":"ICCV: International Conference on Computer Vision","location":"Seoul, Korea","start_date":"2019-10-27","end_date":"2019-11-02"},"language":[{"iso":"eng"}]},{"date_published":"2019-10-01T00:00:00Z","publication":"Proceedings of the 2019 International Conference on Computer Vision Workshop","citation":{"ama":"Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. Detecting visual relationships using box attention. In: Proceedings of the 2019 International Conference on Computer Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00217","apa":"Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting visual relationships using box attention. In Proceedings of the 2019 International Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217","ieee":"A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual relationships using box attention,” in Proceedings of the 2019 International Conference on Computer Vision Workshop, Seoul, South Korea, 2019.","ista":"Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. 2019. Detecting visual relationships using box attention. Proceedings of the 2019 International Conference on Computer Vision Workshop. ICCVW: International Conference on Computer Vision Workshop, 1749–1753.","short":"A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.","mla":"Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.” Proceedings of the 2019 International Conference on Computer Vision Workshop, 1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217.","chicago":"Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio Ferrari. “Detecting Visual Relationships Using Box Attention.” In Proceedings of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019. https://doi.org/10.1109/ICCVW.2019.00217."},"day":"01","article_processing_charge":"No","scopus_import":"1","oa_version":"Preprint","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"7640","status":"public","title":"Detecting visual relationships using box attention","abstract":[{"lang":"eng","text":"We propose a new model for detecting visual relationships, such as \"person riding motorcycle\" or \"bottle on table\". This task is an important step towards comprehensive structured mage understanding, going beyond detecting individual objects. Our main novelty is a Box Attention mechanism that allows to model pairwise interactions between objects using standard object detection pipelines. The resulting model is conceptually clean, expressive and relies on well-justified training and prediction procedures. Moreover, unlike previously proposed approaches, our model does not introduce any additional complex components or hyperparameters on top of those already required by the underlying detection model. We conduct an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating strong quantitative and qualitative results."}],"type":"conference","conference":{"end_date":"2019-10-28","location":"Seoul, South Korea","start_date":"2019-10-27","name":"ICCVW: International Conference on Computer Vision Workshop"},"doi":"10.1109/ICCVW.2019.00217","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1807.02136"}],"external_id":{"isi":["000554591601098"],"arxiv":["1807.02136"]},"oa":1,"quality_controlled":"1","isi":1,"project":[{"call_identifier":"FP7","name":"Lifelong Learning of Visual Scene Understanding","_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036"}],"month":"10","publication_identifier":{"isbn":["9781728150239"]},"author":[{"full_name":"Kolesnikov, Alexander","last_name":"Kolesnikov","first_name":"Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Kuznetsova, Alina","last_name":"Kuznetsova","first_name":"Alina"},{"first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph"},{"first_name":"Vittorio","last_name":"Ferrari","full_name":"Ferrari, Vittorio"}],"date_created":"2020-04-05T22:00:51Z","date_updated":"2023-09-08T11:18:37Z","year":"2019","publication_status":"published","publisher":"IEEE","department":[{"_id":"ChLa"}],"ec_funded":1,"article_number":"1749-1753"},{"scopus_import":"1","article_processing_charge":"No","has_accepted_license":"1","day":"13","citation":{"ista":"Phuong M, Lampert C. 2019. Towards understanding knowledge distillation. Proceedings of the 36th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 97, 5142–5151.","ieee":"M. Phuong and C. Lampert, “Towards understanding knowledge distillation,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 5142–5151.","apa":"Phuong, M., & Lampert, C. (2019). Towards understanding knowledge distillation. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 5142–5151). Long Beach, CA, United States: ML Research Press.","ama":"Phuong M, Lampert C. Towards understanding knowledge distillation. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:5142-5151.","chicago":"Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.” In Proceedings of the 36th International Conference on Machine Learning, 97:5142–51. ML Research Press, 2019.","mla":"Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, ML Research Press, 2019, pp. 5142–51.","short":"M. Phuong, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 5142–5151."},"publication":"Proceedings of the 36th International Conference on Machine Learning","page":"5142-5151","date_published":"2019-06-13T00:00:00Z","type":"conference","abstract":[{"lang":"eng","text":"Knowledge distillation, i.e. one classifier being trained on the outputs of another classifier, is an empirically very successful technique for knowledge transfer between classifiers. It has even been observed that classifiers learn much faster and more reliably if trained with the outputs of another classifier as soft labels, instead of from ground truth data. So far, however, there is no satisfactory theoretical explanation of this phenomenon. In this work, we provide the first insights into the working mechanisms of distillation by studying the special case of linear and deep linear classifiers. Specifically, we prove a generalization bound that establishes fast convergence of the expected risk of a distillation-trained linear classifier. From the bound and its proof we extract three keyfactors that determine the success of distillation: data geometry – geometric properties of the datadistribution, in particular class separation, has an immediate influence on the convergence speed of the risk; optimization bias– gradient descentoptimization finds a very favorable minimum of the distillation objective; and strong monotonicity– the expected risk of the student classifier always decreases when the size of the training set grows."}],"_id":"6569","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 97","ddc":["000"],"status":"public","title":"Towards understanding knowledge distillation","file":[{"relation":"main_file","file_id":"6570","date_created":"2019-06-20T18:22:56Z","date_updated":"2020-07-14T12:47:33Z","checksum":"a66d00e2694d749250f8507f301320ca","file_name":"paper.pdf","access_level":"open_access","file_size":686432,"content_type":"application/pdf","creator":"bphuong"}],"oa_version":"Published Version","month":"06","oa":1,"quality_controlled":"1","conference":{"start_date":"2019-06-10","location":"Long Beach, CA, United States","end_date":"2019-06-15","name":"ICML: International Conference on Machine Learning"},"language":[{"iso":"eng"}],"file_date_updated":"2020-07-14T12:47:33Z","year":"2019","publisher":"ML Research Press","department":[{"_id":"ChLa"}],"publication_status":"published","author":[{"id":"3EC6EE64-F248-11E8-B48F-1D18A9856A87","last_name":"Bui Thi Mai","first_name":"Phuong","full_name":"Bui Thi Mai, Phuong"},{"full_name":"Lampert, Christoph","last_name":"Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"volume":97,"date_created":"2019-06-20T18:23:03Z","date_updated":"2023-10-17T12:31:38Z"},{"page":"3488-3498","citation":{"chicago":"Konstantinov, Nikola H, and Christoph Lampert. “Robust Learning from Untrusted Sources.” In Proceedings of the 36th International Conference on Machine Learning, 97:3488–98. ML Research Press, 2019.","short":"N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.","mla":"Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted Sources.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, ML Research Press, 2019, pp. 3488–98.","apa":"Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.","ieee":"N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.","ista":"Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources. Proceedings of the 36th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 97, 3488–3498.","ama":"Konstantinov NH, Lampert C. Robust learning from untrusted sources. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:3488-3498."},"publication":"Proceedings of the 36th International Conference on Machine Learning","date_published":"2019-06-01T00:00:00Z","scopus_import":"1","article_processing_charge":"No","day":"01","intvolume":" 97","title":"Robust learning from untrusted sources","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"6590","oa_version":"Preprint","type":"conference","abstract":[{"lang":"eng","text":"Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization. "}],"project":[{"grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Lifelong Learning of Visual Scene Understanding"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385","name":"International IST Doctoral Program","call_identifier":"H2020"}],"quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/1901.10310","open_access":"1"}],"oa":1,"external_id":{"arxiv":["1901.10310"]},"language":[{"iso":"eng"}],"conference":{"end_date":"2919-06-15","location":"Long Beach, CA, USA","start_date":"2019-06-10","name":"ICML: International Conference on Machine Learning"},"month":"06","department":[{"_id":"ChLa"}],"publisher":"ML Research Press","publication_status":"published","year":"2019","volume":97,"date_created":"2019-06-27T14:18:23Z","date_updated":"2023-10-17T12:31:55Z","related_material":{"record":[{"id":"10799","status":"public","relation":"dissertation_contains"}]},"author":[{"full_name":"Konstantinov, Nikola H","first_name":"Nikola H","last_name":"Konstantinov","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"ec_funded":1},{"article_processing_charge":"No","day":"14","scopus_import":"1","date_published":"2019-02-14T00:00:00Z","page":"244-259","citation":{"chicago":"Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a ConvNet Operates Outside of Its Specifications,” 11269:244–59. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-12939-2_18.","mla":"Sun, Rémy, and Christoph Lampert. KS(Conf): A Light-Weight Test If a ConvNet Operates Outside of Its Specifications. Vol. 11269, Springer Nature, 2019, pp. 244–59, doi:10.1007/978-3-030-12939-2_18.","short":"R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.","ista":"Sun R, Lampert C. 2019. KS(conf): A light-weight test if a ConvNet operates outside of Its specifications. GCPR: Conference on Pattern Recognition, LNCS, vol. 11269, 244–259.","ieee":"R. Sun and C. Lampert, “KS(conf): A light-weight test if a ConvNet operates outside of Its specifications,” presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany, 2019, vol. 11269, pp. 244–259.","apa":"Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-12939-2_18","ama":"Sun R, Lampert C. KS(conf): A light-weight test if a ConvNet operates outside of Its specifications. In: Vol 11269. Springer Nature; 2019:244-259. doi:10.1007/978-3-030-12939-2_18"},"abstract":[{"lang":"eng","text":"Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for days or even years as components of real-world commercial applications. A major open problem in this context, however, is quality control. Good classification performance can only be expected if systems run under the specific conditions, in particular data distributions, that they were trained for. Surprisingly, none of the currently used deep network architectures have a built-in functionality that could detect if a network operates on data from a distribution it was not trained for, such that potentially a warning to the human users could be triggered. In this work, we describe KS(conf), a procedure for detecting such outside of specifications (out-of-specs) operation, based on statistical testing of the network outputs. We show by extensive experiments using the ImageNet, AwA2 and DAVIS datasets on a variety of ConvNets architectures that KS(conf) reliably detects out-of-specs situations. It furthermore has a number of properties that make it a promising candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with all networks, including pretrained ones, and requires no a priori knowledge of how the data distribution could change. "}],"alternative_title":["LNCS"],"type":"conference","oa_version":"Preprint","intvolume":" 11269","title":"KS(conf): A light-weight test if a ConvNet operates outside of Its specifications","status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"6482","publication_identifier":{"isbn":["9783030129385","9783030129392"],"eissn":["1611-3349"],"issn":["0302-9743"]},"month":"02","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-12939-2_18","conference":{"end_date":"2018-10-12","location":"Stuttgart, Germany","start_date":"2018-10-09","name":"GCPR: Conference on Pattern Recognition"},"project":[{"_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7"}],"quality_controlled":"1","external_id":{"arxiv":["1804.04171"]},"oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/1804.04171","open_access":"1"}],"ec_funded":1,"volume":11269,"date_updated":"2024-02-22T14:57:29Z","date_created":"2019-05-24T09:48:36Z","related_material":{"record":[{"id":"6944","relation":"later_version","status":"public"}]},"author":[{"last_name":"Sun","first_name":"Rémy","full_name":"Sun, Rémy"},{"full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph"}],"publisher":"Springer Nature","department":[{"_id":"ChLa"}],"publication_status":"published","year":"2019"},{"type":"dissertation","alternative_title":["ISTA Thesis"],"abstract":[{"lang":"eng","text":"The most common assumption made in statistical learning theory is the assumption of the independent and identically distributed (i.i.d.) data. While being very convenient mathematically, it is often very clearly violated in practice. This disparity between the machine learning theory and applications underlies a growing demand in the development of algorithms that learn from dependent data and theory that can provide generalization guarantees similar to the independent situations. This thesis is dedicated to two variants of dependencies that can arise in practice. One is a dependence on the level of samples in a single learning task. Another dependency type arises in the multi-task setting when the tasks are dependent on each other even though the data for them can be i.i.d. In both cases we model the data (samples or tasks) as stochastic processes and introduce new algorithms for both settings that take into account and exploit the resulting dependencies. We prove the theoretical guarantees on the performance of the introduced algorithms under different evaluation criteria and, in addition, we compliment the theoretical study by the empirical one, where we evaluate some of the algorithms on two real world datasets to highlight their practical applicability."}],"_id":"68","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","ddc":["004","519"],"title":"Learning from dependent data","status":"public","pubrep_id":"1048","oa_version":"Published Version","file":[{"access_level":"open_access","file_name":"2018_Thesis_Zimin.pdf","creator":"dernst","content_type":"application/pdf","file_size":1036137,"file_id":"6253","relation":"main_file","checksum":"e849dd40a915e4d6c5572b51b517f098","date_updated":"2020-07-14T12:47:40Z","date_created":"2019-04-09T07:32:47Z"},{"access_level":"closed","file_name":"2018_Thesis_Zimin_Source.zip","file_size":637490,"content_type":"application/zip","creator":"dernst","relation":"source_file","file_id":"6254","checksum":"da092153cec55c97461bd53c45c5d139","date_updated":"2020-07-14T12:47:40Z","date_created":"2019-04-09T07:32:47Z"}],"article_processing_charge":"No","has_accepted_license":"1","day":"01","citation":{"mla":"Zimin, Alexander. Learning from Dependent Data. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048.","short":"A. Zimin, Learning from Dependent Data, Institute of Science and Technology Austria, 2018.","chicago":"Zimin, Alexander. “Learning from Dependent Data.” Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:TH1048.","ama":"Zimin A. Learning from dependent data. 2018. doi:10.15479/AT:ISTA:TH1048","ista":"Zimin A. 2018. Learning from dependent data. Institute of Science and Technology Austria.","apa":"Zimin, A. (2018). Learning from dependent data. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH1048","ieee":"A. Zimin, “Learning from dependent data,” Institute of Science and Technology Austria, 2018."},"page":"92","date_published":"2018-09-01T00:00:00Z","publist_id":"7986","ec_funded":1,"file_date_updated":"2020-07-14T12:47:40Z","year":"2018","publisher":"Institute of Science and Technology Austria","department":[{"_id":"ChLa"}],"publication_status":"published","author":[{"first_name":"Alexander","last_name":"Zimin","id":"37099E9C-F248-11E8-B48F-1D18A9856A87","full_name":"Zimin, Alexander"}],"date_created":"2018-12-11T11:44:27Z","date_updated":"2023-09-07T12:29:07Z","publication_identifier":{"issn":["2663-337X"]},"month":"09","oa":1,"project":[{"call_identifier":"FP7","name":"Lifelong Learning of Visual Scene Understanding","_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036"}],"doi":"10.15479/AT:ISTA:TH1048","language":[{"iso":"eng"}],"supervisor":[{"full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph"}],"degree_awarded":"PhD"},{"month":"05","publication_identifier":{"issn":["2663-337X"]},"degree_awarded":"PhD","supervisor":[{"last_name":"Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"language":[{"iso":"eng"}],"doi":"10.15479/AT:ISTA:th_1021","project":[{"grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425","name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7"}],"oa":1,"file_date_updated":"2020-07-14T12:45:22Z","ec_funded":1,"publist_id":"7718","date_created":"2018-12-11T11:45:09Z","date_updated":"2023-09-07T12:51:46Z","author":[{"full_name":"Kolesnikov, Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87","last_name":"Kolesnikov","first_name":"Alexander"}],"publication_status":"published","publisher":"Institute of Science and Technology Austria","department":[{"_id":"ChLa"}],"acknowledgement":"I also gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.","year":"2018","day":"25","has_accepted_license":"1","article_processing_charge":"No","date_published":"2018-05-25T00:00:00Z","page":"113","citation":{"chicago":"Kolesnikov, Alexander. “Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images.” Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:th_1021.","mla":"Kolesnikov, Alexander. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th_1021.","short":"A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, Institute of Science and Technology Austria, 2018.","ista":"Kolesnikov A. 2018. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. Institute of Science and Technology Austria.","ieee":"A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images,” Institute of Science and Technology Austria, 2018.","apa":"Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021","ama":"Kolesnikov A. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. 2018. doi:10.15479/AT:ISTA:th_1021"},"abstract":[{"lang":"eng","text":"Modern computer vision systems heavily rely on statistical machine learning models, which typically require large amounts of labeled data to be learned reliably. Moreover, very recently computer vision research widely adopted techniques for representation learning, which further increase the demand for labeled data. However, for many important practical problems there is relatively small amount of labeled data available, so it is problematic to leverage full potential of the representation learning methods. One way to overcome this obstacle is to invest substantial resources into producing large labelled datasets. Unfortunately, this can be prohibitively expensive in practice. In this thesis we focus on the alternative way of tackling the aforementioned issue. We concentrate on methods, which make use of weakly-labeled or even unlabeled data. Specifically, the first half of the thesis is dedicated to the semantic image segmentation task. We develop a technique, which achieves competitive segmentation performance and only requires annotations in a form of global image-level labels instead of dense segmentation masks. Subsequently, we present a new methodology, which further improves segmentation performance by leveraging tiny additional feedback from a human annotator. By using our methods practitioners can greatly reduce the amount of data annotation effort, which is required to learn modern image segmentation models. In the second half of the thesis we focus on methods for learning from unlabeled visual data. We study a family of autoregressive models for modeling structure of natural images and discuss potential applications of these models. Moreover, we conduct in-depth study of one of these applications, where we develop the state-of-the-art model for the probabilistic image colorization task."}],"alternative_title":["ISTA Thesis"],"type":"dissertation","file":[{"file_size":12918758,"content_type":"application/pdf","creator":"system","file_name":"IST-2018-1021-v1+1_thesis-unsigned-pdfa.pdf","access_level":"open_access","date_created":"2018-12-12T10:14:57Z","date_updated":"2020-07-14T12:45:22Z","checksum":"bc678e02468d8ebc39dc7267dfb0a1c4","relation":"main_file","file_id":"5113"},{"file_id":"6225","relation":"source_file","checksum":"bc66973b086da5a043f1162dcfb1fde4","date_updated":"2020-07-14T12:45:22Z","date_created":"2019-04-05T09:34:49Z","access_level":"closed","file_name":"2018_Thesis_Kolesnikov_source.zip","creator":"dernst","file_size":55973760,"content_type":"application/zip"}],"oa_version":"Published Version","pubrep_id":"1021","status":"public","ddc":["004"],"title":"Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images","_id":"197","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1"},{"month":"03","isi":1,"quality_controlled":"1","oa":1,"main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/205484v1","open_access":"1"}],"external_id":{"isi":["000426219600025"]},"language":[{"iso":"eng"}],"doi":"10.1534/genetics.117.300638","publist_id":"7251","department":[{"_id":"NiBa"},{"_id":"ChLa"}],"publisher":"Genetics Society of America","publication_status":"published","year":"2018","volume":208,"date_updated":"2023-09-11T13:42:38Z","date_created":"2018-12-11T11:47:12Z","related_material":{"record":[{"id":"200","status":"public","relation":"dissertation_contains"}]},"author":[{"full_name":"Ringbauer, Harald","id":"417FCFF4-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4884-9682","first_name":"Harald","last_name":"Ringbauer"},{"full_name":"Kolesnikov, Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87","last_name":"Kolesnikov","first_name":"Alexander"},{"full_name":"Field, David","first_name":"David","last_name":"Field"},{"full_name":"Barton, Nicholas H","first_name":"Nicholas H","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8548-5240"}],"scopus_import":"1","article_processing_charge":"No","day":"01","page":"1231-1245","citation":{"short":"H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.","mla":"Ringbauer, Harald, et al. “Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns.” Genetics, vol. 208, no. 3, Genetics Society of America, 2018, pp. 1231–45, doi:10.1534/genetics.117.300638.","chicago":"Ringbauer, Harald, Alexander Kolesnikov, David Field, and Nicholas H Barton. “Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns.” Genetics. Genetics Society of America, 2018. https://doi.org/10.1534/genetics.117.300638.","ama":"Ringbauer H, Kolesnikov A, Field D, Barton NH. Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. 2018;208(3):1231-1245. doi:10.1534/genetics.117.300638","apa":"Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.117.300638","ieee":"H. Ringbauer, A. Kolesnikov, D. Field, and N. H. Barton, “Estimating barriers to gene flow from distorted isolation-by-distance patterns,” Genetics, vol. 208, no. 3. Genetics Society of America, pp. 1231–1245, 2018.","ista":"Ringbauer H, Kolesnikov A, Field D, Barton NH. 2018. Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. 208(3), 1231–1245."},"publication":"Genetics","date_published":"2018-03-01T00:00:00Z","type":"journal_article","issue":"3","abstract":[{"text":"In continuous populations with local migration, nearby pairs of individuals have on average more similar genotypes\r\nthan geographically well separated pairs. A barrier to gene flow distorts this classical pattern of isolation by distance. Genetic similarity is decreased for sample pairs on different sides of the barrier and increased for pairs on the same side near the barrier. Here, we introduce an inference scheme that utilizes this signal to detect and estimate the strength of a linear barrier to gene flow in two-dimensions. We use a diffusion approximation to model the effects of a barrier on the geographical spread of ancestry backwards in time. This approach allows us to calculate the chance of recent coalescence and probability of identity by descent. We introduce an inference scheme that fits these theoretical results to the geographical covariance structure of bialleleic genetic markers. It can estimate the strength of the barrier as well as several demographic parameters. We investigate the power of our inference scheme to detect barriers by applying it to a wide range of simulated data. We also showcase an example application to a Antirrhinum majus (snapdragon) flower color hybrid zone, where we do not detect any signal of a strong genome wide barrier to gene flow.","lang":"eng"}],"intvolume":" 208","title":"Estimating barriers to gene flow from distorted isolation-by-distance patterns","status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"563","oa_version":"Preprint"},{"language":[{"iso":"eng"}],"doi":"10.1109/TPAMI.2018.2804998","quality_controlled":"1","isi":1,"external_id":{"isi":["000428901200001"]},"oa":1,"month":"05","volume":40,"date_created":"2018-12-11T11:45:48Z","date_updated":"2023-09-11T14:07:54Z","author":[{"full_name":"Darrell, Trevor","last_name":"Darrell","first_name":"Trevor"},{"orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph"},{"full_name":"Sebe, Nico","last_name":"Sebe","first_name":"Nico"},{"last_name":"Wu","first_name":"Ying","full_name":"Wu, Ying"},{"full_name":"Yan, Yan","last_name":"Yan","first_name":"Yan"}],"publisher":"IEEE","department":[{"_id":"ChLa"}],"publication_status":"published","year":"2018","publist_id":"7544","file_date_updated":"2020-07-14T12:46:03Z","date_published":"2018-05-01T00:00:00Z","page":"1029 - 1031","article_type":"original","citation":{"chicago":"Darrell, Trevor, Christoph Lampert, Nico Sebe, Ying Wu, and Yan Yan. “Guest Editors’ Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2018.2804998.","mla":"Darrell, Trevor, et al. “Guest Editors’ Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, IEEE, 2018, pp. 1029–31, doi:10.1109/TPAMI.2018.2804998.","short":"T. Darrell, C. Lampert, N. Sebe, Y. Wu, Y. Yan, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1029–1031.","ista":"Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. 2018. Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(5), 1029–1031.","ieee":"T. Darrell, C. Lampert, N. Sebe, Y. Wu, and Y. Yan, “Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5. IEEE, pp. 1029–1031, 2018.","apa":"Darrell, T., Lampert, C., Sebe, N., Wu, Y., & Yan, Y. (2018). Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2018.2804998","ama":"Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(5):1029-1031. doi:10.1109/TPAMI.2018.2804998"},"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","article_processing_charge":"No","has_accepted_license":"1","day":"01","scopus_import":"1","file":[{"creator":"dernst","file_size":141724,"content_type":"application/pdf","access_level":"open_access","file_name":"2018_IEEE_Darrell.pdf","checksum":"b19c75da06faf3291a3ca47dfa50ef63","date_updated":"2020-07-14T12:46:03Z","date_created":"2020-05-14T12:50:48Z","file_id":"7835","relation":"main_file"}],"oa_version":"Published Version","intvolume":" 40","title":"Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis","status":"public","ddc":["000"],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"321","issue":"5","abstract":[{"text":"The twelve papers in this special section focus on learning systems with shared information for computer vision and multimedia communication analysis. In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes containing a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with shared information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different levels of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems.","lang":"eng"}],"type":"journal_article"},{"isi":1,"quality_controlled":"1","external_id":{"arxiv":["1712.08087"],"isi":["000457843609036"]},"main_file_link":[{"url":" https://doi.org/10.48550/arXiv.1712.08087","open_access":"1"}],"oa":1,"language":[{"iso":"eng"}],"conference":{"end_date":"2018-06-23","start_date":"2018-06-18","location":"Salt Lake City, UT, United States","name":"CVF: Conference on Computer Vision and Pattern Recognition"},"doi":"10.1109/cvpr.2018.00956","month":"12","publication_identifier":{"eissn":["2575-7075"],"isbn":["9781538664209"]},"publication_status":"published","department":[{"_id":"ChLa"}],"publisher":"IEEE","year":"2018","date_created":"2022-03-18T12:45:09Z","date_updated":"2023-09-19T15:11:49Z","author":[{"first_name":"Jasper","last_name":"Uijlings","full_name":"Uijlings, Jasper"},{"last_name":"Konyushkova","first_name":"Ksenia","full_name":"Konyushkova, Ksenia"},{"orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph"},{"first_name":"Vittorio","last_name":"Ferrari","full_name":"Ferrari, Vittorio"}],"page":"9175-9184","publication":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","citation":{"short":"J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184.","mla":"Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–84, doi:10.1109/cvpr.2018.00956.","chicago":"Uijlings, Jasper, Ksenia Konyushkova, Christoph Lampert, and Vittorio Ferrari. “Learning Intelligent Dialogs for Bounding Box Annotation.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9175–84. IEEE, 2018. https://doi.org/10.1109/cvpr.2018.00956.","ama":"Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs for bounding box annotation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2018:9175-9184. doi:10.1109/cvpr.2018.00956","apa":"Uijlings, J., Konyushkova, K., Lampert, C., & Ferrari, V. (2018). Learning intelligent dialogs for bounding box annotation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9175–9184). Salt Lake City, UT, United States: IEEE. https://doi.org/10.1109/cvpr.2018.00956","ieee":"J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent dialogs for bounding box annotation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, United States, 2018, pp. 9175–9184.","ista":"Uijlings J, Konyushkova K, Lampert C, Ferrari V. 2018. Learning intelligent dialogs for bounding box annotation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVF: Conference on Computer Vision and Pattern Recognition, 9175–9184."},"date_published":"2018-12-17T00:00:00Z","scopus_import":"1","day":"17","article_processing_charge":"No","status":"public","title":"Learning intelligent dialogs for bounding box annotation","_id":"10882","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","oa_version":"Preprint","type":"conference","abstract":[{"lang":"eng","text":"We introduce Intelligent Annotation Dialogs for bounding box annotation. We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding box in a minimal amount of time. Specifically, we consider two actions: box verification [34], where the annotator verifies a box generated by an object detector, and manual box drawing. We explore two kinds of agents, one based on predicting the probability that a box will be positively verified, and the other based on reinforcement learning. We demonstrate that (1) our agents are able to learn efficient annotation strategies in several scenarios, automatically adapting to the image difficulty, the desired quality of the boxes, and the detector strength; (2) in all scenarios the resulting annotation dialogs speed up annotation compared to manual box drawing alone and box verification alone, while also outperforming any fixed combination of verification and drawing in most scenarios; (3) in a realistic scenario where the detector is iteratively re-trained, our agents evolve a series of strategies that reflect the shifting trade-off between verification and drawing as the detector grows stronger."}]},{"abstract":[{"text":"We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.","lang":"eng"}],"type":"conference","oa_version":"Preprint","intvolume":" 80","status":"public","title":"Learning equations for extrapolation and control","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"6012","article_processing_charge":"No","day":"01","scopus_import":"1","date_published":"2018-02-01T00:00:00Z","page":"4442-4450","citation":{"ama":"Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:4442-4450.","apa":"Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for extrapolation and control. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research Press.","ieee":"S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation and control,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.","ista":"Sahoo S, Lampert C, Martius GS. 2018. Learning equations for extrapolation and control. Proceedings of the 35th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 80, 4442–4450.","short":"S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.","mla":"Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 4442–50.","chicago":"Sahoo, Subham, Christoph Lampert, and Georg S Martius. “Learning Equations for Extrapolation and Control.” In Proceedings of the 35th International Conference on Machine Learning, 80:4442–50. ML Research Press, 2018."},"publication":"Proceedings of the 35th International Conference on Machine Learning","ec_funded":1,"volume":80,"date_created":"2019-02-14T15:21:07Z","date_updated":"2023-10-17T09:50:53Z","related_material":{"link":[{"relation":"press_release","description":"News on IST Homepage","url":"https://ist.ac.at/en/news/first-machine-learning-method-capable-of-accurate-extrapolation/"}]},"author":[{"full_name":"Sahoo, Subham","first_name":"Subham","last_name":"Sahoo"},{"full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph"},{"full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","first_name":"Georg S"}],"publisher":"ML Research Press","department":[{"_id":"ChLa"}],"publication_status":"published","year":"2018","month":"02","language":[{"iso":"eng"}],"conference":{"name":"ICML: International Conference on Machine Learning","start_date":"2018-07-10","location":"Stockholm, Sweden","end_date":"2018-07-15"},"project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"isi":1,"quality_controlled":"1","oa":1,"external_id":{"isi":["000683379204058"],"arxiv":["1806.07259"]},"main_file_link":[{"url":"https://arxiv.org/abs/1806.07259","open_access":"1"}]},{"day":"01","article_processing_charge":"No","scopus_import":"1","date_published":"2018-02-01T00:00:00Z","page":"2815-2824","publication":"Proceedings of the 35 th International Conference on Machine Learning","citation":{"ieee":"I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient descent,” in Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824.","apa":"Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic gradient descent. In Proceedings of the 35 th International Conference on Machine Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: ML Research Press.","ista":"Kuzborskij I, Lampert C. 2018. Data-dependent stability of stochastic gradient descent. Proceedings of the 35 th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 80, 2815–2824.","ama":"Kuzborskij I, Lampert C. Data-dependent stability of stochastic gradient descent. In: Proceedings of the 35 Th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:2815-2824.","chicago":"Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic Gradient Descent.” In Proceedings of the 35 Th International Conference on Machine Learning, 80:2815–24. ML Research Press, 2018.","short":"I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference on Machine Learning, ML Research Press, 2018, pp. 2815–2824.","mla":"Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic Gradient Descent.” Proceedings of the 35 Th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 2815–24."},"abstract":[{"text":"We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to previous distribution-free algorithmic stability results for SGD which depend on the worst-case constants. By virtue of the data-dependent argument, our bounds provide new insights into learning with SGD on convex and non-convex problems. In the convex case, we show that the bound on the generalization error depends on the risk at the initialization point. In the non-convex case, we prove that the expected curvature of the objective function around the initialization point has crucial influence on the generalization error. In both cases, our results suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization. As a corollary, our results allow us to show optimistic generalization bounds that exhibit fast convergence rates for SGD subject to a vanishing empirical risk and low noise of stochastic gradient. ","lang":"eng"}],"type":"conference","oa_version":"Preprint","status":"public","title":"Data-dependent stability of stochastic gradient descent","intvolume":" 80","_id":"6011","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"02","language":[{"iso":"eng"}],"conference":{"name":"ICML: International Conference on Machine Learning","end_date":"2018-07-15","start_date":"2018-07-10","location":"Stockholm, Sweden"},"quality_controlled":"1","isi":1,"project":[{"_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036","name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7"}],"oa":1,"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1703.01678"}],"external_id":{"isi":["000683379202095"],"arxiv":["1703.01678"]},"ec_funded":1,"date_updated":"2023-10-17T09:51:13Z","date_created":"2019-02-14T14:51:57Z","volume":80,"author":[{"last_name":"Kuzborskij","first_name":"Ilja","full_name":"Kuzborskij, Ilja"},{"last_name":"Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"publication_status":"published","department":[{"_id":"ChLa"}],"publisher":"ML Research Press","year":"2018"},{"_id":"6589","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","title":"The convergence of sparsified gradient methods","oa_version":"Preprint","type":"conference","abstract":[{"text":"Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of communication-reduction methods, such as quantization, large-batch methods, and gradient sparsification, have been proposed. To date, gradient sparsification methods--where each node sorts gradients by magnitude, and only communicates a subset of the components, accumulating the rest locally--are known to yield some of the largest practical gains. Such methods can reduce the amount of communication per step by up to \\emph{three orders of magnitude}, while preserving model accuracy. Yet, this family of methods currently has no theoretical justification. This is the question we address in this paper. We prove that, under analytic assumptions, sparsifying gradients by magnitude with local error correction provides convergence guarantees, for both convex and non-convex smooth objectives, for data-parallel SGD. The main insight is that sparsification methods implicitly maintain bounds on the maximum impact of stale updates, thanks to selection by magnitude. Our analysis and empirical validation also reveal that these methods do require analytical conditions to converge well, justifying existing heuristics.","lang":"eng"}],"publication":"Advances in Neural Information Processing Systems 31","citation":{"mla":"Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural Information Processing Systems Foundation, 2018, pp. 5973–83.","short":"D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat, C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural Information Processing Systems Foundation, 2018, pp. 5973–5983.","chicago":"Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.","ama":"Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.","ista":"Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.","ieee":"D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.","apa":"Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation."},"page":"5973-5983","date_published":"2018-12-01T00:00:00Z","scopus_import":"1","day":"01","article_processing_charge":"No","year":"2018","publication_status":"published","publisher":"Neural Information Processing Systems Foundation","department":[{"_id":"DaAl"},{"_id":"ChLa"}],"author":[{"full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","first_name":"Dan-Adrian"},{"full_name":"Hoefler, Torsten","last_name":"Hoefler","first_name":"Torsten"},{"last_name":"Johansson","first_name":"Mikael","full_name":"Johansson, Mikael"},{"first_name":"Nikola H","last_name":"Konstantinov","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","full_name":"Konstantinov, Nikola H"},{"first_name":"Sarit","last_name":"Khirirat","full_name":"Khirirat, Sarit"},{"full_name":"Renggli, Cedric","first_name":"Cedric","last_name":"Renggli"}],"date_created":"2019-06-27T09:32:55Z","date_updated":"2023-10-17T11:47:20Z","volume":"Volume 2018","ec_funded":1,"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1809.10505"}],"oa":1,"external_id":{"arxiv":["1809.10505"],"isi":["000461852000047"]},"isi":1,"quality_controlled":"1","project":[{"grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","call_identifier":"H2020"}],"conference":{"name":"NeurIPS: Conference on Neural Information Processing Systems","end_date":"2018-12-08","location":"Montreal, Canada","start_date":"2018-12-02"},"language":[{"iso":"eng"}],"month":"12"}]