[{"date_created":"2023-03-23T14:33:13Z","year":"2022","_id":"12750","arxiv":1,"license":"https://creativecommons.org/licenses/by-nc-sa/4.0/","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2210.15607"}],"oa_version":"Preprint","article_processing_charge":"No","publication_status":"submitted","department":[{"_id":"GradSch"},{"_id":"MaSe"}],"related_material":{"record":[{"status":"public","id":"12732","relation":"dissertation_contains"},{"status":"public","id":"14334","relation":"later_version"}]},"month":"11","abstract":[{"lang":"eng","text":"Quantum kinetically constrained models have recently attracted significant attention due to their anomalous dynamics and thermalization. In this work, we introduce a hitherto unexplored family of kinetically constrained models featuring a conserved particle number and strong inversion-symmetry breaking due to facilitated hopping. We demonstrate that these models provide a generic example of so-called quantum Hilbert space fragmentation, that is manifested in disconnected sectors in the Hilbert space that are not apparent in the computational basis. Quantum Hilbert space fragmentation leads to an exponential in system size number of eigenstates with exactly zero entanglement entropy across several bipartite cuts. These eigenstates can be probed dynamically using quenches from simple initial product states. In addition, we study the particle spreading under unitary dynamics launched from the domain wall state, and find faster than diffusive dynamics at high particle densities, that crosses over into logarithmically slow relaxation at smaller densities. Using a classically simulable cellular automaton, we reproduce the logarithmic dynamics observed in the quantum case. Our work suggests that particle conserving constrained models with inversion symmetry breaking realize so far unexplored universality classes of dynamics and invite their further theoretical and experimental studies."}],"day":"07","publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_updated":"2023-09-20T10:46:29Z","type":"preprint","author":[{"last_name":"Brighi","first_name":"Pietro","id":"4115AF5C-F248-11E8-B48F-1D18A9856A87","full_name":"Brighi, Pietro","orcid":"0000-0002-7969-2729"},{"first_name":"Marko","last_name":"Ljubotina","orcid":"0000-0003-0038-7068","id":"F75EE9BE-5C90-11EA-905D-16643DDC885E","full_name":"Ljubotina, Marko"},{"first_name":"Maksym","last_name":"Serbyn","orcid":"0000-0002-2399-5827","id":"47809E7E-F248-11E8-B48F-1D18A9856A87","full_name":"Serbyn, Maksym"}],"citation":{"ista":"Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. arXiv, 2210.15607.","chicago":"Brighi, Pietro, Marko Ljubotina, and Maksym Serbyn. “Hilbert Space Fragmentation and Slow Dynamics in Particle-Conserving Quantum East Models.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2210.15607\">https://doi.org/10.48550/arXiv.2210.15607</a>.","apa":"Brighi, P., Ljubotina, M., &#38; Serbyn, M. (n.d.). Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2210.15607\">https://doi.org/10.48550/arXiv.2210.15607</a>","ieee":"P. Brighi, M. Ljubotina, and M. Serbyn, “Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models,” <i>arXiv</i>. .","ama":"Brighi P, Ljubotina M, Serbyn M. Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2210.15607\">10.48550/arXiv.2210.15607</a>","mla":"Brighi, Pietro, et al. “Hilbert Space Fragmentation and Slow Dynamics in Particle-Conserving Quantum East Models.” <i>ArXiv</i>, 2210.15607, doi:<a href=\"https://doi.org/10.48550/arXiv.2210.15607\">10.48550/arXiv.2210.15607</a>.","short":"P. Brighi, M. Ljubotina, M. Serbyn, ArXiv (n.d.)."},"article_number":"2210.15607","doi":"10.48550/arXiv.2210.15607","date_published":"2022-11-07T00:00:00Z","language":[{"iso":"eng"}],"tmp":{"image":"/images/cc_by_nc_sa.png","short":"CC BY-NC-SA (4.0)","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"},"status":"public","external_id":{"arxiv":["2210.15607"]},"title":"Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models"},{"acknowledgement":"Kush Grover: The author has been supported by the DFG research training group GRK\r\n2428 ConVeY.\r\nMaximilian Weininger: The author has been partially supported by DFG projects 383882557\r\nStatistical Unbounded Verification (SUV) and 427755713 Group-By Objectives in Probabilistic\r\nVerification (GOPro)","scopus_import":"1","arxiv":1,"conference":{"location":"Warsaw, Poland","name":"CONCUR: Conference on Concurrency Theory","start_date":"2022-09-13","end_date":"2022-09-16"},"license":"https://creativecommons.org/licenses/by/4.0/","publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","department":[{"_id":"KrCh"}],"month":"09","file_date_updated":"2023-09-26T10:43:15Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference","volume":243,"intvolume":"       243","ddc":["000"],"alternative_title":["LIPIcs"],"doi":"10.4230/LIPIcs.CONCUR.2022.11","date_published":"2022-09-15T00:00:00Z","language":[{"iso":"eng"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","date_created":"2023-03-28T08:09:32Z","year":"2022","_id":"12775","quality_controlled":"1","oa_version":"Published Version","article_processing_charge":"No","publication_status":"published","file":[{"date_updated":"2023-09-26T10:43:15Z","date_created":"2023-09-26T10:43:15Z","relation":"main_file","success":1,"file_id":"14372","file_name":"2022_LIPIcS_Grover.pdf","creator":"dernst","access_level":"open_access","file_size":960036,"content_type":"application/pdf","checksum":"e282e43d3ae0ba6e067b72f4583e13c0"}],"abstract":[{"lang":"eng","text":"We consider the problem of approximating the reachability probabilities in Markov decision processes (MDP) with uncountable (continuous) state and action spaces. While there are algorithms that, for special classes of such MDP, provide a sequence of approximations converging to the true value in the limit, our aim is to obtain an algorithm with guarantees on the precision of the approximation.\r\nAs this problem is undecidable in general, assumptions on the MDP are necessary. Our main contribution is to identify sufficient assumptions that are as weak as possible, thus approaching the \"boundary\" of which systems can be correctly and reliably analyzed. To this end, we also argue why each of our assumptions is necessary for algorithms based on processing finitely many observations.\r\nWe present two solution variants. The first one provides converging lower bounds under weaker assumptions than typical ones from previous works concerned with guarantees. The second one then utilizes stronger assumptions to additionally provide converging upper bounds. Altogether, we obtain an anytime algorithm, i.e. yielding a sequence of approximants with known and iteratively improving precision, converging to the true value in the limit. Besides, due to the generality of our assumptions, our algorithms are very general templates, readily allowing for various heuristics from literature in contrast to, e.g., a specific discretization algorithm. Our theoretical contribution thus paves the way for future practical improvements without sacrificing correctness guarantees."}],"day":"15","publication":"33rd International Conference on Concurrency Theory ","publication_identifier":{"issn":["1868-8969"]},"oa":1,"author":[{"full_name":"Grover, Kush","last_name":"Grover","first_name":"Kush"},{"first_name":"Jan","last_name":"Kretinsky","id":"44CEF464-F248-11E8-B48F-1D18A9856A87","full_name":"Kretinsky, Jan","orcid":"0000-0002-8122-2881"},{"full_name":"Meggendorfer, Tobias","id":"b21b0c15-30a2-11eb-80dc-f13ca25802e1","orcid":"0000-0002-1712-2165","last_name":"Meggendorfer","first_name":"Tobias"},{"first_name":"Maimilian","last_name":"Weininger","full_name":"Weininger, Maimilian"}],"citation":{"chicago":"Grover, Kush, Jan Kretinsky, Tobias Meggendorfer, and Maimilian Weininger. “Anytime Guarantees for Reachability in Uncountable Markov Decision Processes.” In <i>33rd International Conference on Concurrency Theory </i>, Vol. 243. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. <a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.11\">https://doi.org/10.4230/LIPIcs.CONCUR.2022.11</a>.","ista":"Grover K, Kretinsky J, Meggendorfer T, Weininger M. 2022. Anytime guarantees for reachability in uncountable Markov decision processes. 33rd International Conference on Concurrency Theory . CONCUR: Conference on Concurrency Theory, LIPIcs, vol. 243, 11.","mla":"Grover, Kush, et al. “Anytime Guarantees for Reachability in Uncountable Markov Decision Processes.” <i>33rd International Conference on Concurrency Theory </i>, vol. 243, 11, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, doi:<a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.11\">10.4230/LIPIcs.CONCUR.2022.11</a>.","ieee":"K. Grover, J. Kretinsky, T. Meggendorfer, and M. Weininger, “Anytime guarantees for reachability in uncountable Markov decision processes,” in <i>33rd International Conference on Concurrency Theory </i>, Warsaw, Poland, 2022, vol. 243.","apa":"Grover, K., Kretinsky, J., Meggendorfer, T., &#38; Weininger, M. (2022). Anytime guarantees for reachability in uncountable Markov decision processes. In <i>33rd International Conference on Concurrency Theory </i> (Vol. 243). Warsaw, Poland: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.11\">https://doi.org/10.4230/LIPIcs.CONCUR.2022.11</a>","ama":"Grover K, Kretinsky J, Meggendorfer T, Weininger M. Anytime guarantees for reachability in uncountable Markov decision processes. In: <i>33rd International Conference on Concurrency Theory </i>. Vol 243. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022. doi:<a href=\"https://doi.org/10.4230/LIPIcs.CONCUR.2022.11\">10.4230/LIPIcs.CONCUR.2022.11</a>","short":"K. Grover, J. Kretinsky, T. Meggendorfer, M. Weininger, in:, 33rd International Conference on Concurrency Theory , Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022."},"date_updated":"2023-09-26T10:43:30Z","has_accepted_license":"1","article_number":"11","external_id":{"arxiv":["2008.04824"]},"title":"Anytime guarantees for reachability in uncountable Markov decision processes"},{"department":[{"_id":"TiBr"}],"publisher":"State University of New York","month":"08","acknowledgement":"This work was begun while the author was participating in the programme on \"Diophantine equations\" at the Hausdorff Research Institute for Mathematics in Bonn in 2009. The hospitality and financial support of the institute is gratefully acknowledged. The idea of using conic bundles to study the split del Pezzo surface of degree 5 was explained to the author by Professor Salberger. The author is very grateful to him for his input into this project and also to Shuntaro Yamagishi for many useful comments on an earlier version of this manuscript. While working on this paper the author was supported by FWF grant P32428-N35.","page":"1193 - 1229","intvolume":"        28","ddc":["510"],"language":[{"iso":"eng"}],"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2022-08-24T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-03-30T07:09:35Z","volume":28,"type":"journal_article","oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","file":[{"file_name":"2022_NYJM_Browning.pdf","date_created":"2023-03-30T07:09:35Z","success":1,"relation":"main_file","file_id":"12778","date_updated":"2023-03-30T07:09:35Z","checksum":"c01e8291794a1bdb7416aa103cb68ef8","content_type":"application/pdf","file_size":897267,"creator":"dernst","access_level":"open_access"}],"abstract":[{"text":"An improved asymptotic formula is established for the number of rational points of bounded height on the split smooth del Pezzo surface of degree 5. The proof uses the five conic bundle structures on the surface.","lang":"eng"}],"publication_status":"published","article_type":"original","year":"2022","_id":"12776","date_created":"2023-03-28T09:21:09Z","has_accepted_license":"1","project":[{"name":"New frontiers of the Manin conjecture","_id":"26AEDAB2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P32428"}],"title":"Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5","publication":"New York Journal of Mathematics","publication_identifier":{"issn":["1076-9803"]},"oa":1,"day":"24","date_updated":"2023-10-18T07:59:13Z","citation":{"short":"T.D. Browning, New York Journal of Mathematics 28 (2022) 1193–1229.","chicago":"Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del Pezzo Surface of Degree 5.” <i>New York Journal of Mathematics</i>. State University of New York, 2022.","ista":"Browning TD. 2022. Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. New York Journal of Mathematics. 28, 1193–1229.","mla":"Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del Pezzo Surface of Degree 5.” <i>New York Journal of Mathematics</i>, vol. 28, State University of New York, 2022, pp. 1193–229.","apa":"Browning, T. D. (2022). Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. <i>New York Journal of Mathematics</i>. State University of New York.","ama":"Browning TD. Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5. <i>New York Journal of Mathematics</i>. 2022;28:1193-1229.","ieee":"T. D. Browning, “Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree 5,” <i>New York Journal of Mathematics</i>, vol. 28. State University of New York, pp. 1193–1229, 2022."},"author":[{"first_name":"Timothy D","last_name":"Browning","orcid":"0000-0002-8314-0177","id":"35827D50-F248-11E8-B48F-1D18A9856A87","full_name":"Browning, Timothy D"}]},{"external_id":{"arxiv":["2111.08617"]},"title":"CGX: Adaptive system support for communication-efficient deep learning","has_accepted_license":"1","citation":{"short":"I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd ACM/IFIP International Middleware Conference, Association for Computing Machinery, 2022, pp. 241–254.","ama":"Markov I, Ramezanikebrya H, Alistarh D-A. CGX: Adaptive system support for communication-efficient deep learning. In: <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>. Association for Computing Machinery; 2022:241-254. doi:<a href=\"https://doi.org/10.1145/3528535.3565248\">10.1145/3528535.3565248</a>","ieee":"I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>, Quebec, QC, Canada, 2022, pp. 241–254.","apa":"Markov, I., Ramezanikebrya, H., &#38; Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i> (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3528535.3565248\">https://doi.org/10.1145/3528535.3565248</a>","mla":"Markov, Ilia, et al. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.” <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>, Association for Computing Machinery, 2022, pp. 241–54, doi:<a href=\"https://doi.org/10.1145/3528535.3565248\">10.1145/3528535.3565248</a>.","ista":"Markov I, Ramezanikebrya H, Alistarh D-A. 2022. CGX: Adaptive system support for communication-efficient deep learning. Proceedings of the 23rd ACM/IFIP International Middleware Conference. Middleware: International Middleware Conference, 241–254.","chicago":"Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.” In <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>, 241–54. Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3528535.3565248\">https://doi.org/10.1145/3528535.3565248</a>."},"author":[{"id":"D0CF4148-C985-11E9-8066-0BDEE5697425","full_name":"Markov, Ilia","last_name":"Markov","first_name":"Ilia"},{"last_name":"Ramezanikebrya","first_name":"Hamidreza","full_name":"Ramezanikebrya, Hamidreza"},{"last_name":"Alistarh","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X"}],"date_updated":"2023-04-03T06:21:04Z","oa":1,"publication":"Proceedings of the 23rd ACM/IFIP International Middleware Conference","publication_identifier":{"isbn":["9781450393409"]},"day":"01","abstract":[{"text":"The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-to-point communication, and in particular via hardware bandwidth over-provisioning. Overprovisioning comes at a cost: there is an order of magnitude price difference between \"cloud-grade\" servers with such support, relative to their popular \"consumer-grade\" counterparts, although single server-grade and consumer-grade GPUs can have similar computational envelopes.\r\n\r\nIn this paper, we show that the costly hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for compressed communication in ML applications, for both multi-GPU single-node training, as well as larger-scale multi-node training. CGX is based on two technical advances: At the system level, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication. At the application level, it provides seamless, parameter-free integration with popular frameworks, so that end-users do not have to modify training recipes, nor significant training code. This is complemented by a layer-wise adaptive compression technique which dynamically balances compression gains with accuracy preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.","lang":"eng"}],"file":[{"date_updated":"2023-04-03T06:17:58Z","file_name":"2022_ACMMiddleware_Markov.pdf","success":1,"relation":"main_file","date_created":"2023-04-03T06:17:58Z","file_id":"12795","creator":"dernst","access_level":"open_access","checksum":"1a397746235f245da5468819247ff663","file_size":1514169,"content_type":"application/pdf"}],"publication_status":"published","article_processing_charge":"Yes (via OA deal)","oa_version":"Published Version","quality_controlled":"1","_id":"12780","year":"2022","date_created":"2023-03-31T06:17:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","language":[{"iso":"eng"}],"date_published":"2022-11-01T00:00:00Z","doi":"10.1145/3528535.3565248","ddc":["000"],"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-04-03T06:17:58Z","month":"11","publisher":"Association for Computing Machinery","department":[{"_id":"DaAl"}],"page":"241-254","conference":{"end_date":"2022-11-11","location":"Quebec, QC, Canada","start_date":"2022-11-07","name":"Middleware: International Middleware Conference"},"arxiv":1,"acknowledgement":"The authors sincerely thank Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler and Bapi Chatterjee for useful discussions throughout the development of this project."},{"external_id":{"isi":["000954466300006"],"arxiv":["2109.10245"]},"title":" A coarse geometric expansion of a variant of Arthur's truncated traces and some applications","project":[{"name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411","call_identifier":"H2020"}],"issue":"1","author":[{"last_name":"Yu","first_name":"Hongjie","orcid":"0000-0001-5128-7126","id":"3D7DD9BE-F248-11E8-B48F-1D18A9856A87","full_name":"Yu, Hongjie"}],"date_updated":"2023-08-04T10:42:38Z","citation":{"short":"H. Yu, Pacific Journal of Mathematics 321 (2022) 193–237.","apa":"Yu, H. (2022).  A coarse geometric expansion of a variant of Arthur’s truncated traces and some applications. <i>Pacific Journal of Mathematics</i>. Mathematical Sciences Publishers. <a href=\"https://doi.org/10.2140/pjm.2022.321.193\">https://doi.org/10.2140/pjm.2022.321.193</a>","ieee":"H. Yu, “ A coarse geometric expansion of a variant of Arthur’s truncated traces and some applications,” <i>Pacific Journal of Mathematics</i>, vol. 321, no. 1. Mathematical Sciences Publishers, pp. 193–237, 2022.","ama":"Yu H.  A coarse geometric expansion of a variant of Arthur’s truncated traces and some applications. <i>Pacific Journal of Mathematics</i>. 2022;321(1):193-237. doi:<a href=\"https://doi.org/10.2140/pjm.2022.321.193\">10.2140/pjm.2022.321.193</a>","mla":"Yu, Hongjie. “ A Coarse Geometric Expansion of a Variant of Arthur’s Truncated Traces and Some Applications.” <i>Pacific Journal of Mathematics</i>, vol. 321, no. 1, Mathematical Sciences Publishers, 2022, pp. 193–237, doi:<a href=\"https://doi.org/10.2140/pjm.2022.321.193\">10.2140/pjm.2022.321.193</a>.","ista":"Yu H. 2022.  A coarse geometric expansion of a variant of Arthur’s truncated traces and some applications. Pacific Journal of Mathematics. 321(1), 193–237.","chicago":"Yu, Hongjie. “ A Coarse Geometric Expansion of a Variant of Arthur’s Truncated Traces and Some Applications.” <i>Pacific Journal of Mathematics</i>. Mathematical Sciences Publishers, 2022. <a href=\"https://doi.org/10.2140/pjm.2022.321.193\">https://doi.org/10.2140/pjm.2022.321.193</a>."},"day":"29","oa":1,"publication_identifier":{"issn":["0030-8730"],"eissn":["1945-5844"]},"publication":"Pacific Journal of Mathematics","article_type":"original","publication_status":"published","abstract":[{"text":"Let F be a global function field with constant field Fq. Let G be a reductive group over Fq. We establish a variant of Arthur's truncated kernel for G and for its Lie algebra which generalizes Arthur's original construction. We establish a coarse geometric expansion for our variant truncation.\r\nAs applications, we consider some existence and uniqueness problems of some cuspidal automorphic representations for the functions field of the projective line P1Fq with two points of ramifications.","lang":"eng"}],"quality_controlled":"1","keyword":["Arthur–Selberg trace formula","cuspidal automorphic representations","global function fields"],"article_processing_charge":"No","oa_version":"Preprint","date_created":"2023-04-02T22:01:11Z","ec_funded":1,"_id":"12793","year":"2022","date_published":"2022-08-29T00:00:00Z","doi":"10.2140/pjm.2022.321.193","status":"public","language":[{"iso":"eng"}],"intvolume":"       321","type":"journal_article","volume":321,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"08","department":[{"_id":"TaHa"}],"publisher":"Mathematical Sciences Publishers","isi":1,"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2109.10245","open_access":"1"}],"arxiv":1,"page":"193-237","acknowledgement":"I’d like to thank Prof. Chaudouard for introducing me to this area. I’d like to thank Prof. Harris for asking me the question that makes Section 10 possible. I’m grateful for the support of Prof. Hausel and IST Austria. The author was funded by an ISTplus fellowship: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 754411.","scopus_import":"1"},{"abstract":[{"text":"Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) to understand, quantify and control this phenomenon. While bounding general memorization can have detrimental effects on the performance of a trained model, bounding RM does not prevent effective learning. The difference is most pronounced when the data distribution is long-tailed, with many queries having only few training examples: Impeding general memorization prevents effective learning, while impeding only relational memorization still allows learning general properties of the underlying concepts. We formalize the notion of Relational Privacy (RP) and, inspired by Differential Privacy (DP), we provide a possible definition of Differential Relational Privacy (DrP). These notions can be used to describe and compute bounds on the amount of RM in a trained model. We illustrate Relational Privacy concepts in experiments with large-scale models for Question Answering.","lang":"eng"}],"department":[{"_id":"GradSch"},{"_id":"MaMo"}],"publication_status":"submitted","month":"03","oa_version":"Preprint","article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2203.16701","open_access":"1"}],"arxiv":1,"year":"2022","_id":"12860","date_created":"2023-04-23T16:11:48Z","language":[{"iso":"eng"}],"external_id":{"arxiv":["2203.16701"]},"title":"Towards differential relational privacy and its use in question answering","status":"public","doi":"10.48550/arXiv.2203.16701","date_published":"2022-03-30T00:00:00Z","article_number":"2203.16701","author":[{"full_name":"Bombari, Simone","id":"ca726dda-de17-11ea-bc14-f9da834f63aa","last_name":"Bombari","first_name":"Simone"},{"first_name":"Alessandro","last_name":"Achille","full_name":"Achille, Alessandro"},{"first_name":"Zijian","last_name":"Wang","full_name":"Wang, Zijian"},{"full_name":"Wang, Yu-Xiang","last_name":"Wang","first_name":"Yu-Xiang"},{"first_name":"Yusheng","last_name":"Xie","full_name":"Xie, Yusheng"},{"full_name":"Singh, Kunwar Yashraj","first_name":"Kunwar Yashraj","last_name":"Singh"},{"full_name":"Appalaraju, Srikar","first_name":"Srikar","last_name":"Appalaraju"},{"last_name":"Mahadevan","first_name":"Vijay","full_name":"Mahadevan, Vijay"},{"first_name":"Stefano","last_name":"Soatto","full_name":"Soatto, Stefano"}],"citation":{"short":"S. Bombari, A. Achille, Z. Wang, Y.-X. Wang, Y. Xie, K.Y. Singh, S. Appalaraju, V. Mahadevan, S. Soatto, ArXiv (n.d.).","mla":"Bombari, Simone, et al. “Towards Differential Relational Privacy and Its Use in Question Answering.” <i>ArXiv</i>, 2203.16701, doi:<a href=\"https://doi.org/10.48550/arXiv.2203.16701\">10.48550/arXiv.2203.16701</a>.","ieee":"S. Bombari <i>et al.</i>, “Towards differential relational privacy and its use in question answering,” <i>arXiv</i>. .","ama":"Bombari S, Achille A, Wang Z, et al. Towards differential relational privacy and its use in question answering. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2203.16701\">10.48550/arXiv.2203.16701</a>","apa":"Bombari, S., Achille, A., Wang, Z., Wang, Y.-X., Xie, Y., Singh, K. Y., … Soatto, S. (n.d.). Towards differential relational privacy and its use in question answering. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2203.16701\">https://doi.org/10.48550/arXiv.2203.16701</a>","chicago":"Bombari, Simone, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, and Stefano Soatto. “Towards Differential Relational Privacy and Its Use in Question Answering.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2203.16701\">https://doi.org/10.48550/arXiv.2203.16701</a>.","ista":"Bombari S, Achille A, Wang Z, Wang Y-X, Xie Y, Singh KY, Appalaraju S, Mahadevan V, Soatto S. Towards differential relational privacy and its use in question answering. arXiv, 2203.16701."},"date_updated":"2023-04-25T07:34:49Z","type":"preprint","publication":"arXiv","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"day":"30"},{"doi":"10.5061/DRYAD.GTHT76HMZ","date_published":"2022-09-02T00:00:00Z","tmp":{"image":"/images/cc_0.png","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"status":"public","title":"Improving genome-wide association discovery and genomic prediction accuracy in biobank data","ddc":["570"],"author":[{"full_name":"Orliac, Etienne","first_name":"Etienne","last_name":"Orliac"},{"last_name":"Trejo Banos","first_name":"Daniel","full_name":"Trejo Banos, Daniel"},{"first_name":"Sven","last_name":"Ojavee","full_name":"Ojavee, Sven"},{"full_name":"Läll, Kristi","last_name":"Läll","first_name":"Kristi"},{"last_name":"Mägi","first_name":"Reedik","full_name":"Mägi, Reedik"},{"full_name":"Visscher, Peter","last_name":"Visscher","first_name":"Peter"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","first_name":"Matthew Richard","last_name":"Robinson"}],"type":"research_data_reference","citation":{"short":"E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R. Robinson, (2022).","ieee":"E. Orliac <i>et al.</i>, “Improving genome-wide association discovery and genomic prediction accuracy in biobank data.” Dryad, 2022.","apa":"Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P., &#38; Robinson, M. R. (2022). Improving genome-wide association discovery and genomic prediction accuracy in biobank data. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>","ama":"Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association discovery and genomic prediction accuracy in biobank data. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>","mla":"Orliac, Etienne, et al. <i>Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","ista":"Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">10.5061/DRYAD.GTHT76HMZ</a>.","chicago":"Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi, Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.GTHT76HMZ\">https://doi.org/10.5061/DRYAD.GTHT76HMZ</a>."},"date_updated":"2023-08-03T12:40:37Z","day":"02","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"related_material":{"record":[{"relation":"used_in_publication","id":"11733","status":"public"}]},"department":[{"_id":"MaRo"}],"publisher":"Dryad","month":"09","abstract":[{"lang":"eng","text":"Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies."}],"license":"https://creativecommons.org/publicdomain/zero/1.0/","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.gtht76hmz"}],"oa_version":"Published Version","article_processing_charge":"No","date_created":"2023-05-23T16:28:13Z","year":"2022","_id":"13064"},{"date_created":"2023-05-23T16:33:12Z","_id":"13066","year":"2022","month":"07","related_material":{"record":[{"id":"12247","status":"public","relation":"used_in_publication"}]},"publisher":"Dryad","department":[{"_id":"NiBa"}],"abstract":[{"lang":"eng","text":"Chromosomal inversions have been shown to play a major role in local adaptation by suppressing recombination between alternative arrangements and maintaining beneficial allele combinations. However, so far, their importance relative to the remaining genome remains largely unknown. Understanding the genetic architecture of adaptation requires better estimates of how loci of different effect sizes contribute to phenotypic variation. Here, we used three Swedish islands where the marine snail Littorina saxatilis has repeatedly evolved into two distinct ecotypes along a habitat transition. We estimated the contribution of inversion polymorphisms to phenotypic divergence while controlling for polygenic effects in the remaining genome using a quantitative genetics framework. We confirmed the importance of inversions but showed that contributions of loci outside inversions are of similar magnitude, with variable proportions dependent on the trait and the population. Some inversions showed consistent effects across all sites, whereas others exhibited site-specific effects, indicating that the genomic basis for replicated phenotypic divergence is only partly shared. The contributions of sexual dimorphism as well as environmental factors to phenotypic variation were significant but minor compared to inversions and polygenic background. Overall, this integrated approach provides insight into the multiple mechanisms contributing to parallel phenotypic divergence."}],"main_file_link":[{"url":"https://doi.org/10.5061/dryad.m905qfv4b","open_access":"1"}],"article_processing_charge":"No","oa_version":"Published Version","date_updated":"2023-08-04T09:42:10Z","type":"research_data_reference","citation":{"mla":"Koch, Eva, et al. <i>Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>.","ama":"Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. 2022. doi:<a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>","ieee":"E. Koch, M. Ravinet, A. M. Westram, K. Jonannesson, and R. Butlin, “Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution.” Dryad, 2022.","apa":"Koch, E., Ravinet, M., Westram, A. M., Jonannesson, K., &#38; Butlin, R. (2022). Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">https://doi.org/10.5061/DRYAD.M905QFV4B</a>","chicago":"Koch, Eva, Mark Ravinet, Anja M Westram, Kerstin Jonannesson, and Roger Butlin. “Data from: Genetic Architecture of Repeated Phenotypic Divergence in Littorina Saxatilis Ecotype Evolution.” Dryad, 2022. <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">https://doi.org/10.5061/DRYAD.M905QFV4B</a>.","ista":"Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. 2022. Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.M905QFV4B\">10.5061/DRYAD.M905QFV4B</a>.","short":"E. Koch, M. Ravinet, A.M. Westram, K. Jonannesson, R. Butlin, (2022)."},"author":[{"last_name":"Koch","first_name":"Eva","full_name":"Koch, Eva"},{"full_name":"Ravinet, Mark","first_name":"Mark","last_name":"Ravinet"},{"orcid":"0000-0003-1050-4969","full_name":"Westram, Anja M","id":"3C147470-F248-11E8-B48F-1D18A9856A87","first_name":"Anja M","last_name":"Westram"},{"full_name":"Jonannesson, Kerstin","last_name":"Jonannesson","first_name":"Kerstin"},{"first_name":"Roger","last_name":"Butlin","full_name":"Butlin, Roger"}],"day":"28","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-07-28T00:00:00Z","doi":"10.5061/DRYAD.M905QFV4B","tmp":{"image":"/images/cc_0.png","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"title":"Data from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution","status":"public","ddc":["570"]},{"date_created":"2023-05-23T17:05:40Z","_id":"13076","year":"2022","month":"01","related_material":{"link":[{"url":"https://github.com/npostnikova/mq-based-schedulers/tree/v1.1","relation":"software"}],"record":[{"relation":"used_in_publication","id":"11180","status":"public"}]},"publisher":"Zenodo","department":[{"_id":"DaAl"}],"abstract":[{"text":"The source code for replicating experiments presented in the paper.\r\n\r\nThe implementation of the designed priority schedulers can be found in Galois-2.2.1/include/Galois/WorkList/:\r\nStealingMultiQueue.h is the StealingMultiQueue.\r\nMQOptimized/ contains MQ Optimized variants.\r\n\r\nWe provide images that contain all the dependencies and datasets. Images can be pulled from npostnikova/mq-based-schedulers repository, or downloaded from Zenodo. See readme for more detail.","lang":"eng"}],"main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5813846","open_access":"1"}],"article_processing_charge":"No","oa_version":"Published Version","type":"research_data_reference","citation":{"short":"A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).","mla":"Postnikova, Anastasiia, et al. <i>Multi-Queues Can Be State-of-the-Art Priority Schedulers</i>. Zenodo, 2022, doi:<a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>.","apa":"Postnikova, A., Koval, N., Nadiradze, G., &#38; Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5733408\">https://doi.org/10.5281/ZENODO.5733408</a>","ama":"Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:<a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>","ieee":"A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.","chicago":"Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. <a href=\"https://doi.org/10.5281/ZENODO.5733408\">https://doi.org/10.5281/ZENODO.5733408</a>.","ista":"Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5733408\">10.5281/ZENODO.5733408</a>."},"author":[{"full_name":"Postnikova, Anastasiia","first_name":"Anastasiia","last_name":"Postnikova"},{"id":"2F4DB10C-F248-11E8-B48F-1D18A9856A87","full_name":"Koval, Nikita","first_name":"Nikita","last_name":"Koval"},{"id":"3279A00C-F248-11E8-B48F-1D18A9856A87","full_name":"Nadiradze, Giorgi","first_name":"Giorgi","last_name":"Nadiradze"},{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh"}],"date_updated":"2023-08-03T06:48:34Z","day":"03","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2022-01-03T00:00:00Z","doi":"10.5281/ZENODO.5733408","status":"public","title":"Multi-queues can be state-of-the-art priority schedulers","ddc":["510"]},{"file":[{"creator":"dernst","access_level":"open_access","file_size":585135,"content_type":"application/pdf","checksum":"7530a93ef42e10b4db1e5e4b69796e93","date_updated":"2023-07-18T06:32:38Z","date_created":"2023-07-18T06:32:38Z","success":1,"relation":"main_file","file_id":"13243","file_name":"2022_PMLR_vanderPlas.pdf"}],"abstract":[{"text":"Brains are thought to engage in predictive learning - learning to predict upcoming stimuli - to construct an internal model of their environment. This is especially notable for spatial navigation, as first described by Tolman’s latent learning tasks. However, predictive learning has also been observed in sensory cortex, in settings unrelated to spatial navigation. Apart from normative frameworks such as active inference or efficient coding, what could be the utility of learning to predict the patterns of occurrence of correlated stimuli? Here we show that prediction, and thereby the construction of an internal model of sequential stimuli, can bootstrap the learning process of a working memory task in a recurrent neural network. We implemented predictive learning alongside working memory match-tasks, and networks emerged to solve the prediction task first by encoding information across time to predict upcoming stimuli, and then eavesdropped on this solution to solve the matching task. Eavesdropping was most beneficial when neural resources were limited. Hence, predictive learning acts as a general neural mechanism to learn to store sensory information that can later be essential for working memory tasks.","lang":"eng"}],"publication_status":"published","oa_version":"Published Version","article_processing_charge":"No","quality_controlled":"1","year":"2022","_id":"13239","date_created":"2023-07-16T22:01:12Z","ec_funded":1,"project":[{"name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","grant_number":"819603","call_identifier":"H2020","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234"}],"title":"Predictive learning enables neural networks to learn complex working memory tasks","has_accepted_license":"1","date_updated":"2023-07-18T06:36:28Z","citation":{"short":"T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 518–531.","ista":"Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables neural networks to learn complex working memory tasks. Proceedings of Machine Learning Research. vol. 199, 518–531.","chicago":"Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In <i>Proceedings of Machine Learning Research</i>, 199:518–31. ML Research Press, 2022.","apa":"Van Der Plas, T. L., Vogels, T. P., &#38; Manohar, S. G. (2022). Predictive learning enables neural networks to learn complex working memory tasks. In <i>Proceedings of Machine Learning Research</i> (Vol. 199, pp. 518–531). ML Research Press.","ieee":"T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning enables neural networks to learn complex working memory tasks,” in <i>Proceedings of Machine Learning Research</i>, 2022, vol. 199, pp. 518–531.","ama":"Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural networks to learn complex working memory tasks. In: <i>Proceedings of Machine Learning Research</i>. Vol 199. ML Research Press; 2022:518-531.","mla":"Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” <i>Proceedings of Machine Learning Research</i>, vol. 199, ML Research Press, 2022, pp. 518–31."},"author":[{"last_name":"Van Der Plas","first_name":"Thijs L.","full_name":"Van Der Plas, Thijs L."},{"orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","last_name":"Vogels","first_name":"Tim P"},{"full_name":"Manohar, Sanjay G.","first_name":"Sanjay G.","last_name":"Manohar"}],"publication_identifier":{"eissn":["2640-3498"]},"publication":"Proceedings of Machine Learning Research","oa":1,"day":"01","department":[{"_id":"TiVo"}],"publisher":"ML Research Press","month":"12","page":"518-531","scopus_import":"1","acknowledgement":"The authors would like to thank members of the Vogels lab and Manohar lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X and Leverhulme Grant RPG-2018-310.","language":[{"iso":"eng"}],"status":"public","date_published":"2022-12-01T00:00:00Z","ddc":["000"],"intvolume":"       199","volume":199,"type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-07-18T06:32:38Z"},{"language":[{"iso":"eng"}],"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"doi":"10.3389/ffunb.2022.1029114","date_published":"2022-10-19T00:00:00Z","intvolume":"         3","ddc":["579"],"volume":3,"type":"journal_article","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-07-17T11:46:34Z","publisher":"Frontiers Media","department":[{"_id":"JiFr"}],"month":"10","scopus_import":"1","acknowledgement":"The research leading to these results received funding from the European Research Council under the European Union’s Seventh Framework Programme ERC-2013-STG (grant agreement: 335691), the Austrian Science Fund (I 3033-B22), the Austrian Academy of Sciences, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy EXC-2070-390732324 (PhenoRob) and DFG grant (DJ 64/5-1).\r\nWe would like to thank the GMI/IMBA/IMP core facilities for their excellent technical support. We would like to acknowledge Dr. Sinéad A. O’Sullivan from DZNE, University of Bonn for providing anti-GFP antibodies. The authors are thankful to the Excellence University of Bonn for providing infrastructure and instrumentation facilities at the INRES-Plant Pathology department.","title":"Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis","article_number":"1029114","has_accepted_license":"1","author":[{"last_name":"Ingole","first_name":"Kishor D.","full_name":"Ingole, Kishor D."},{"first_name":"Nithya","last_name":"Nagarajan","full_name":"Nagarajan, Nithya"},{"first_name":"Simon","last_name":"Uhse","full_name":"Uhse, Simon"},{"id":"e3fdddd5-f6e0-11ea-865d-ca99ee6367f4","full_name":"Giannini, Caterina","first_name":"Caterina","last_name":"Giannini"},{"full_name":"Djamei, Armin","first_name":"Armin","last_name":"Djamei"}],"date_updated":"2024-03-06T14:01:57Z","citation":{"apa":"Ingole, K. D., Nagarajan, N., Uhse, S., Giannini, C., &#38; Djamei, A. (2022). Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. <i>Frontiers in Fungal Biology</i>. Frontiers Media. <a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">https://doi.org/10.3389/ffunb.2022.1029114</a>","ieee":"K. D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, and A. Djamei, “Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis,” <i>Frontiers in Fungal Biology</i>, vol. 3. Frontiers Media, 2022.","ama":"Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. <i>Frontiers in Fungal Biology</i>. 2022;3. doi:<a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">10.3389/ffunb.2022.1029114</a>","mla":"Ingole, Kishor D., et al. “Tetracycline-Controlled (TetON) Gene Expression System for the Smut Fungus Ustilago Maydis.” <i>Frontiers in Fungal Biology</i>, vol. 3, 1029114, Frontiers Media, 2022, doi:<a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">10.3389/ffunb.2022.1029114</a>.","ista":"Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. 2022. Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers in Fungal Biology. 3, 1029114.","chicago":"Ingole, Kishor D., Nithya Nagarajan, Simon Uhse, Caterina Giannini, and Armin Djamei. “Tetracycline-Controlled (TetON) Gene Expression System for the Smut Fungus Ustilago Maydis.” <i>Frontiers in Fungal Biology</i>. Frontiers Media, 2022. <a href=\"https://doi.org/10.3389/ffunb.2022.1029114\">https://doi.org/10.3389/ffunb.2022.1029114</a>.","short":"K.D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, A. Djamei, Frontiers in Fungal Biology 3 (2022)."},"publication":"Frontiers in Fungal Biology","publication_identifier":{"eissn":["2673-6128"]},"oa":1,"day":"19","file":[{"checksum":"2254e0119c0749d6f7237084fefcece6","file_size":27966699,"content_type":"application/pdf","access_level":"open_access","creator":"dernst","file_name":"2023_FrontiersFungalBio_Ingole.pdf","file_id":"13242","date_created":"2023-07-17T11:46:34Z","success":1,"relation":"main_file","date_updated":"2023-07-17T11:46:34Z"}],"abstract":[{"text":"Ustilago maydis is a biotrophic phytopathogenic fungus that causes corn smut disease. As a well-established model system, U. maydis is genetically fully accessible with large omics datasets available and subject to various biological questions ranging from DNA-repair, RNA-transport, and protein secretion to disease biology. For many genetic approaches, tight control of transgene regulation is important. Here we established an optimised version of the Tetracycline-ON (TetON) system for U. maydis. We demonstrate the Tetracycline concentration-dependent expression of fluorescent protein transgenes and the system’s suitability for the induced expression of the toxic protein BCL2 Associated X-1 (Bax1). The Golden Gate compatible vector system contains a native minimal promoter from the mating factor a-1 encoding gene, mfa with ten copies of the tet-regulated operator (tetO) and a codon optimised Tet-repressor (tetR*) which is translationally fused to the native transcriptional corepressor Mql1 (UMAG_05501). The metabolism-independent transcriptional regulator system is functional both, in liquid culture as well as on solid media in the presence of the inducer and can become a useful tool for toxin-antitoxin studies, identification of antifungal proteins, and to study functions of toxic gene products in Ustilago maydis.","lang":"eng"}],"article_type":"original","publication_status":"published","oa_version":"Published Version","article_processing_charge":"Yes","quality_controlled":"1","year":"2022","_id":"13240","date_created":"2023-07-16T22:01:12Z"},{"main_file_link":[{"url":"https://arxiv.org/abs/2102.06004","open_access":"1"}],"publisher":"ML Research Press","department":[{"_id":"ChLa"}],"month":"12","acknowledgement":"This paper is a shortened, workshop version of Konstantinov and Lampert (2021),\r\nhttps://arxiv.org/abs/2102.06004. For further results, including an analysis of algorithms achieving the lower bounds from this paper, we refer to the full version.","scopus_import":"1","arxiv":1,"page":"59-83","intvolume":"       171","date_published":"2022-12-01T00:00:00Z","language":[{"iso":"eng"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference","volume":171,"quality_controlled":"1","oa_version":"Preprint","article_processing_charge":"No","publication_status":"published","related_material":{"record":[{"relation":"extended_version","status":"public","id":"10802"}]},"abstract":[{"text":"Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. Many approaches for training fair models from data have been developed and an implicit assumption about such algorithms is that they are able to recover a fair model, despite potential historical biases in the data. In this work we show a number of impossibility results that indicate that there is no learning algorithm that can recover a fair model when a proportion of the dataset is subject to arbitrary manipulations. Specifically, we prove that there are situations in which an adversary can force any learner to return a biased classifier, with or without degrading accuracy, and that the strength of this bias increases for learning problems with underrepresented protected groups in the data. Our results emphasize on the importance of studying further data corruption models of various strength and of establishing stricter data collection practices for fairness-aware learning.","lang":"eng"}],"date_created":"2023-07-16T22:01:13Z","year":"2022","_id":"13241","title":"On the impossibility of fairness-aware learning from corrupted data","external_id":{"arxiv":["2102.06004"]},"day":"01","publication":"Proceedings of Machine Learning Research","publication_identifier":{"eissn":["2640-3498"]},"oa":1,"citation":{"short":"N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83.","ieee":"N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in <i>Proceedings of Machine Learning Research</i>, 2022, vol. 171, pp. 59–83.","apa":"Konstantinov, N. H., &#38; Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In <i>Proceedings of Machine Learning Research</i> (Vol. 171, pp. 59–83). ML Research Press.","ama":"Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning from corrupted data. In: <i>Proceedings of Machine Learning Research</i>. Vol 171. ML Research Press; 2022:59-83.","mla":"Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” <i>Proceedings of Machine Learning Research</i>, vol. 171, ML Research Press, 2022, pp. 59–83.","ista":"Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.","chicago":"Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” In <i>Proceedings of Machine Learning Research</i>, 171:59–83. ML Research Press, 2022."},"author":[{"last_name":"Konstantinov","first_name":"Nikola H","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","full_name":"Konstantinov, Nikola H"},{"first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"date_updated":"2023-09-26T10:44:37Z"},{"scopus_import":"1","conference":{"name":"AISTATS: Conference on Artificial Intelligence and Statistics","location":"Virtual","start_date":"2022-03-28","end_date":"2022-03-30"},"page":"8439-8457","arxiv":1,"main_file_link":[{"url":"https://arxiv.org/abs/2202.13212","open_access":"1"}],"publisher":"ML Research Press","department":[{"_id":"FrLo"}],"month":"04","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":151,"type":"conference","intvolume":"       151","alternative_title":["PMLR"],"extern":"1","language":[{"iso":"eng"}],"status":"public","date_published":"2022-04-01T00:00:00Z","year":"2022","_id":"14093","date_created":"2023-08-21T09:27:43Z","oa_version":"Preprint","article_processing_charge":"No","quality_controlled":"1","abstract":[{"lang":"eng","text":" We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully increasing the batch size over the course of the algorithm’s execution, which leads to computing full gradients. In contrast, the proposed method, equipped with a stochastic average gradient (SAG) estimator, requires only one sample per iteration. Nevertheless, it guarantees fast convergence rates on par with more sophisticated variance reduction techniques. In applications we put special emphasis on problems with a large number of separable constraints. Such problems are prevalent among semidefinite programming (SDP) formulations arising in machine learning and theoretical computer science. We provide numerical experiments on matrix completion, unsupervised clustering, and sparsest-cut SDPs. "}],"publication_status":"published","publication":"Proceedings of the 25th International Conference on Artificial Intelligence and Statistics","publication_identifier":{"issn":["2640-3498"]},"oa":1,"day":"01","citation":{"mla":"Dresdner, Gideon, et al. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>, vol. 151, ML Research Press, 2022, pp. 8439–57.","ama":"Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A.  Faster one-sample stochastic conditional gradient method for composite convex minimization. In: <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>. Vol 151. ML Research Press; 2022:8439-8457.","ieee":"G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever, “ Faster one-sample stochastic conditional gradient method for composite convex minimization,” in <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>, Virtual, 2022, vol. 151, pp. 8439–8457.","apa":"Dresdner, G., Vladarean, M.-L., Rätsch, G., Locatello, F., Cevher, V., &#38; Yurtsever, A. (2022).  Faster one-sample stochastic conditional gradient method for composite convex minimization. In <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i> (Vol. 151, pp. 8439–8457). Virtual: ML Research Press.","chicago":"Dresdner, Gideon, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, and Alp Yurtsever. “ Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” In <i>Proceedings of the 25th International Conference on Artificial Intelligence and Statistics</i>, 151:8439–57. ML Research Press, 2022.","ista":"Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. 2022.  Faster one-sample stochastic conditional gradient method for composite convex minimization. Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 151, 8439–8457.","short":"G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever, in:, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2022, pp. 8439–8457."},"author":[{"first_name":"Gideon","last_name":"Dresdner","full_name":"Dresdner, Gideon"},{"first_name":"Maria-Luiza","last_name":"Vladarean","full_name":"Vladarean, Maria-Luiza"},{"full_name":"Rätsch, Gunnar","first_name":"Gunnar","last_name":"Rätsch"},{"last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"},{"first_name":"Volkan","last_name":"Cevher","full_name":"Cevher, Volkan"},{"last_name":"Yurtsever","first_name":"Alp","full_name":"Yurtsever, Alp"}],"date_updated":"2023-09-06T10:28:17Z","title":" Faster one-sample stochastic conditional gradient method for composite convex minimization","external_id":{"arxiv":["2202.13212"]}},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":35,"type":"conference","intvolume":"        35","alternative_title":["Advances in Neural Information Processing Systems"],"extern":"1","language":[{"iso":"eng"}],"status":"public","date_published":"2022-12-15T00:00:00Z","scopus_import":"1","conference":{"location":"New Orleans, LA, United States","name":"NeurIPS: Neural Information Processing Systems","start_date":"2022-11-28","end_date":"2022-12-09"},"page":"16548-16562","arxiv":1,"main_file_link":[{"url":"https://arxiv.org/abs/2204.04440","open_access":"1"}],"department":[{"_id":"FrLo"}],"publisher":"Neural Information Processing Systems Foundation","month":"12","publication":"36th Conference on Neural Information Processing Systems","publication_identifier":{"isbn":["9781713871088"]},"oa":1,"day":"15","author":[{"last_name":"Lohaus","first_name":"Michael","full_name":"Lohaus, Michael"},{"first_name":"Matthäus","last_name":"Kleindessner","full_name":"Kleindessner, Matthäus"},{"full_name":"Kenthapadi, Krishnaram","last_name":"Kenthapadi","first_name":"Krishnaram"},{"first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"}],"citation":{"chicago":"Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” In <i>36th Conference on Neural Information Processing Systems</i>, 35:16548–62. Neural Information Processing Systems Foundation, 2022.","ista":"Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. 2022. Are two heads the same as one? Identifying disparate treatment in fair neural networks. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 35, 16548–16562.","mla":"Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” <i>36th Conference on Neural Information Processing Systems</i>, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 16548–62.","ama":"Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads the same as one? Identifying disparate treatment in fair neural networks. In: <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. Neural Information Processing Systems Foundation; 2022:16548-16562.","ieee":"M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are two heads the same as one? Identifying disparate treatment in fair neural networks,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2022, vol. 35, pp. 16548–16562.","apa":"Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., &#38; Russell, C. (2022). Are two heads the same as one? Identifying disparate treatment in fair neural networks. In <i>36th Conference on Neural Information Processing Systems</i> (Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information Processing Systems Foundation.","short":"M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 16548–16562."},"date_updated":"2023-09-06T10:29:42Z","external_id":{"arxiv":["2204.04440"]},"title":"Are two heads the same as one? Identifying disparate treatment in fair neural networks","year":"2022","_id":"14106","date_created":"2023-08-21T12:12:42Z","oa_version":"Preprint","article_processing_charge":"No","quality_controlled":"1","abstract":[{"text":"We show that deep networks trained to satisfy demographic parity often do so\r\nthrough a form of race or gender awareness, and that the more we force a network\r\nto be fair, the more accurately we can recover race or gender from the internal state\r\nof the network. Based on this observation, we investigate an alternative fairness\r\napproach: we add a second classification head to the network to explicitly predict\r\nthe protected attribute (such as race or gender) alongside the original task. After\r\ntraining the two-headed network, we enforce demographic parity by merging the\r\ntwo heads, creating a network with the same architecture as the original network.\r\nWe establish a close relationship between existing approaches and our approach\r\nby showing (1) that the decisions of a fair classifier are well-approximated by our\r\napproach, and (2) that an unfair and optimally accurate classifier can be recovered\r\nfrom a fair classifier and our second head predicting the protected attribute. We use\r\nour explicit formulation to argue that the existing fairness approaches, just as ours,\r\ndemonstrate disparate treatment and that they are likely to be unlawful in a wide\r\nrange of scenarios under US law.","lang":"eng"}],"publication_status":"published"},{"extern":"1","doi":"10.48550/arXiv.2210.12733","date_published":"2022-10-23T00:00:00Z","language":[{"iso":"eng"}],"external_id":{"arxiv":["2210.12733"]},"title":"Self-supervised amodal video object segmentation","status":"public","day":"23","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"36th Conference on Neural Information Processing Systems","oa":1,"citation":{"ista":"Yao J, Hong Y, Wang C, Xiao T, He T, Locatello F, Wipf D, Fu Y, Zhang Z. 2022. Self-supervised amodal video object segmentation. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.","chicago":"Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.” In <i>36th Conference on Neural Information Processing Systems</i>, 2022. <a href=\"https://doi.org/10.48550/arXiv.2210.12733\">https://doi.org/10.48550/arXiv.2210.12733</a>.","ieee":"J. Yao <i>et al.</i>, “Self-supervised amodal video object segmentation,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2022.","ama":"Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation. In: <i>36th Conference on Neural Information Processing Systems</i>. ; 2022. doi:<a href=\"https://doi.org/10.48550/arXiv.2210.12733\">10.48550/arXiv.2210.12733</a>","apa":"Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022). Self-supervised amodal video object segmentation. In <i>36th Conference on Neural Information Processing Systems</i>. New Orleans, LA, United States. <a href=\"https://doi.org/10.48550/arXiv.2210.12733\">https://doi.org/10.48550/arXiv.2210.12733</a>","mla":"Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” <i>36th Conference on Neural Information Processing Systems</i>, 2022, doi:<a href=\"https://doi.org/10.48550/arXiv.2210.12733\">10.48550/arXiv.2210.12733</a>.","short":"J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z. Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022."},"date_updated":"2023-09-11T09:34:17Z","author":[{"full_name":"Yao, Jian","last_name":"Yao","first_name":"Jian"},{"full_name":"Hong, Yuxin","last_name":"Hong","first_name":"Yuxin"},{"last_name":"Wang","first_name":"Chiyu","full_name":"Wang, Chiyu"},{"full_name":"Xiao, Tianjun","last_name":"Xiao","first_name":"Tianjun"},{"last_name":"He","first_name":"Tong","full_name":"He, Tong"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Wipf, David","last_name":"Wipf","first_name":"David"},{"last_name":"Fu","first_name":"Yanwei","full_name":"Fu, Yanwei"},{"last_name":"Zhang","first_name":"Zheng","full_name":"Zhang, Zheng"}],"type":"conference","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2210.12733"}],"oa_version":"Preprint","article_processing_charge":"No","department":[{"_id":"FrLo"}],"publication_status":"published","month":"10","abstract":[{"text":"Amodal perception requires inferring the full shape of an object that is partially occluded. This task is particularly challenging on two levels: (1) it requires more information than what is contained in the instant retina or imaging sensor, (2) it is difficult to obtain enough well-annotated amodal labels for supervision. To this end, this paper develops a new framework of\r\nSelf-supervised amodal Video object segmentation (SaVos). Our method efficiently leverages the visual information of video temporal sequences to infer the amodal mask of objects. The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning paradigm that efficiently utilizes the visible object parts as the supervision to guide the training on videos. In addition to learning type prior to complete masks for known types, SaVos also learns the spatiotemporal prior, which is also useful for the amodal task and could generalize to unseen types. The proposed\r\nframework achieves the state-of-the-art performance on the synthetic amodal segmentation benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends itself well to being transferred to novel distributions using test-time adaptation, outperforming existing models even after the transfer to a new distribution.","lang":"eng"}],"date_created":"2023-08-21T12:13:25Z","year":"2022","_id":"14107","arxiv":1,"conference":{"location":"New Orleans, LA, United States","start_date":"2022-11-28","name":"NeurIPS: Neural Information Processing Systems","end_date":"2022-12-01"}},{"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2203.04913"}],"department":[{"_id":"FrLo"}],"publisher":"Institute of Electrical and Electronics Engineers","month":"07","scopus_import":"1","arxiv":1,"page":"10400-10411","conference":{"name":"CVPR: Conference on Computer Vision and Pattern Recognition","start_date":"2022-06-18","location":"New Orleans, LA, United States","end_date":"2022-06-24"},"extern":"1","doi":"10.1109/cvpr52688.2022.01016","date_published":"2022-07-01T00:00:00Z","language":[{"iso":"eng"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"conference","quality_controlled":"1","oa_version":"Preprint","article_processing_charge":"No","publication_status":"published","abstract":[{"text":"Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups.","lang":"eng"}],"date_created":"2023-08-21T12:18:00Z","year":"2022","_id":"14114","title":"Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers","external_id":{"arxiv":["2203.04913"]},"day":"01","publication_identifier":{"issn":["1063-6919"],"isbn":["9781665469470"],"eissn":["2575-7075"]},"publication":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition","oa":1,"author":[{"last_name":"Zietlow","first_name":"Dominik","full_name":"Zietlow, Dominik"},{"first_name":"Michael","last_name":"Lohaus","full_name":"Lohaus, Michael"},{"last_name":"Balakrishnan","first_name":"Guha","full_name":"Balakrishnan, Guha"},{"first_name":"Matthaus","last_name":"Kleindessner","full_name":"Kleindessner, Matthaus"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco"},{"full_name":"Scholkopf, Bernhard","last_name":"Scholkopf","first_name":"Bernhard"},{"last_name":"Russell","first_name":"Chris","full_name":"Russell, Chris"}],"citation":{"short":"D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.","ista":"Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.","chicago":"Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner, Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 10400–411. Institute of Electrical and Electronics Engineers, 2022. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">https://doi.org/10.1109/cvpr52688.2022.01016</a>.","ieee":"D. Zietlow <i>et al.</i>, “Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers,” in <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, New Orleans, LA, United States, 2022, pp. 10400–10411.","apa":"Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Scholkopf, B., &#38; Russell, C. (2022). Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i> (pp. 10400–10411). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">https://doi.org/10.1109/cvpr52688.2022.01016</a>","ama":"Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In: <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>. Institute of Electrical and Electronics Engineers; 2022:10400-10411. doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">10.1109/cvpr52688.2022.01016</a>","mla":"Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” <i>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–11, doi:<a href=\"https://doi.org/10.1109/cvpr52688.2022.01016\">10.1109/cvpr52688.2022.01016</a>."},"date_updated":"2023-09-11T09:19:14Z"},{"conference":{"end_date":"2022-12-01","start_date":"2022-11-29","location":"New Orleans, United States","name":"NeurIPS: Neural Information Processing Systems"},"arxiv":1,"year":"2022","_id":"14168","date_created":"2023-08-22T13:57:27Z","abstract":[{"lang":"eng","text":"Recent work has seen the development of general purpose neural architectures\r\nthat can be trained to perform tasks across diverse data modalities. General\r\npurpose models typically make few assumptions about the underlying\r\ndata-structure and are known to perform well in the large-data regime. At the\r\nsame time, there has been growing interest in modular neural architectures that\r\nrepresent the data using sparsely interacting modules. These models can be more\r\nrobust out-of-distribution, computationally efficient, and capable of\r\nsample-efficient adaptation to new data. However, they tend to make\r\ndomain-specific assumptions about the data, and present challenges in how\r\nmodule behavior (i.e., parameterization) and connectivity (i.e., their layout)\r\ncan be jointly learned. In this work, we introduce a general purpose, yet\r\nmodular neural architecture called Neural Attentive Circuits (NACs) that\r\njointly learns the parameterization and a sparse connectivity of neural modules\r\nwithout using domain knowledge. NACs are best understood as the combination of\r\ntwo systems that are jointly trained end-to-end: one that determines the module\r\nconfiguration and the other that executes it on an input. We demonstrate\r\nqualitatively that NACs learn diverse and meaningful module configurations on\r\nthe NLVR2 dataset without additional supervision. Quantitatively, we show that\r\nby incorporating modularity in this way, NACs improve upon a strong non-modular\r\nbaseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about\r\n10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that\r\nNACs can achieve an 8x speedup at inference time while losing less than 3%\r\nperformance. Finally, we find NACs to yield competitive results on diverse data\r\nmodalities spanning point-cloud classification, symbolic processing and\r\ntext-classification from ASCII bytes, thereby confirming its general purpose\r\nnature."}],"department":[{"_id":"FrLo"}],"publication_status":"published","month":"10","oa_version":"Preprint","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2210.08031"}],"volume":35,"citation":{"short":"N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E. Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems, 2022.","mla":"Rahaman, Nasim, et al. “Neural Attentive Circuits.” <i>36th Conference on Neural Information Processing Systems</i>, vol. 35, 2022.","apa":"Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B., … Ballas, N. (2022). Neural attentive circuits. In <i>36th Conference on Neural Information Processing Systems</i> (Vol. 35). New Orleans, United States.","ieee":"N. Rahaman <i>et al.</i>, “Neural attentive circuits,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, United States, 2022, vol. 35.","ama":"Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. ; 2022.","chicago":"Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.” In <i>36th Conference on Neural Information Processing Systems</i>, Vol. 35, 2022.","ista":"Rahaman N, Weiss M, Locatello F, Pal C, Bengio Y, Schölkopf B, Li LE, Ballas N. 2022. Neural attentive circuits. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems,  Advances in Neural Information Processing Systems, vol. 35."},"author":[{"last_name":"Rahaman","first_name":"Nasim","full_name":"Rahaman, Nasim"},{"full_name":"Weiss, Martin","last_name":"Weiss","first_name":"Martin"},{"first_name":"Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco"},{"last_name":"Pal","first_name":"Chris","full_name":"Pal, Chris"},{"last_name":"Bengio","first_name":"Yoshua","full_name":"Bengio, Yoshua"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"},{"full_name":"Li, Li Erran","last_name":"Li","first_name":"Li Erran"},{"last_name":"Ballas","first_name":"Nicolas","full_name":"Ballas, Nicolas"}],"date_updated":"2023-09-11T09:29:09Z","type":"conference","publication":"36th Conference on Neural Information Processing Systems","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"day":"14","language":[{"iso":"eng"}],"status":"public","title":"Neural attentive circuits","external_id":{"arxiv":["2210.08031"]},"date_published":"2022-10-14T00:00:00Z","alternative_title":[" Advances in Neural Information Processing Systems"],"intvolume":"        35","extern":"1"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":2022,"type":"conference","alternative_title":["PMLR"],"intvolume":"      2022","extern":"1","language":[{"iso":"eng"}],"status":"public","date_published":"2022-07-22T00:00:00Z","page":"5221-5285","conference":{"name":"International Conference on Machine Learning","start_date":"2022-07-17","location":"Baltimore, MD, United States","end_date":"2022-07-23"},"arxiv":1,"main_file_link":[{"url":"https://arxiv.org/abs/2107.00637","open_access":"1"}],"publisher":"ML Research Press","department":[{"_id":"FrLo"}],"month":"07","publication":"Proceedings of the 39th International Conference on Machine Learning","oa":1,"day":"22","citation":{"chicago":"Dittadi, Andrea, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello. “Generalization and Robustness Implications in Object-Centric Learning.” In <i>Proceedings of the 39th International Conference on Machine Learning</i>, 2022:5221–85. ML Research Press, n.d.","ista":"Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 2022, 5221–5285.","mla":"Dittadi, Andrea, et al. “Generalization and Robustness Implications in Object-Centric Learning.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 2022, ML Research Press, pp. 5221–85.","ieee":"A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello, “Generalization and robustness implications in object-centric learning,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, vol. 2022, pp. 5221–5285.","ama":"Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization and robustness implications in object-centric learning. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 2022. ML Research Press; :5221-5285.","apa":"Dittadi, A., Papa, S., Vita, M. D., Schölkopf, B., Winther, O., &#38; Locatello, F. (n.d.). Generalization and robustness implications in object-centric learning. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 2022, pp. 5221–5285). Baltimore, MD, United States: ML Research Press.","short":"A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, n.d., pp. 5221–5285."},"date_updated":"2023-09-11T10:08:14Z","author":[{"last_name":"Dittadi","first_name":"Andrea","full_name":"Dittadi, Andrea"},{"full_name":"Papa, Samuele","first_name":"Samuele","last_name":"Papa"},{"full_name":"Vita, Michele De","last_name":"Vita","first_name":"Michele De"},{"full_name":"Schölkopf, Bernhard","last_name":"Schölkopf","first_name":"Bernhard"},{"first_name":"Ole","last_name":"Winther","full_name":"Winther, Ole"},{"first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"}],"external_id":{"arxiv":["2107.00637"]},"title":"Generalization and robustness implications in object-centric learning","year":"2022","_id":"14170","date_created":"2023-08-22T13:59:55Z","oa_version":"Preprint","article_processing_charge":"No","quality_controlled":"1","abstract":[{"lang":"eng","text":"The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and performance of downstream tasks in scenes with multiple objects. In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets and evaluate segmentation metrics and downstream object property prediction. In addition, we study generalization and robustness by investigating the settings where either a single object is out of distribution -- e.g., having an unseen color, texture, or shape -- or global properties of the scene are altered -- e.g., by occlusions, cropping, or increasing the number of objects. From our experimental study, we find object-centric representations to be useful for\r\ndownstream tasks and generally robust to most distribution shifts affecting objects. However, when the distribution shift affects the input in a less structured manner, robustness in terms of segmentation and downstream task performance may vary significantly across models and distribution shifts. "}],"publication_status":"submitted"},{"publication_status":"published","abstract":[{"lang":"eng","text":"This paper demonstrates how to recover causal graphs from the score of the\r\ndata distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching algorithms as a building block, we show how to design a new generation\r\nof scalable causal discovery methods. To showcase our approach, we also propose\r\na new efficient method for approximating the score's Jacobian, enabling to\r\nrecover the causal graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive with state-of-the-art causal discovery methods while\r\nbeing significantly faster."}],"quality_controlled":"1","oa_version":"Preprint","article_processing_charge":"No","date_created":"2023-08-22T14:00:18Z","year":"2022","_id":"14171","title":"Score matching enables causal discovery of nonlinear additive noise  models","external_id":{"arxiv":["2203.04413"]},"citation":{"short":"P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753.","mla":"Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 162, ML Research Press, 2022, pp. 18741–53.","apa":"Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., &#38; Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise  models. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.","ama":"Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal discovery of nonlinear additive noise  models. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 162. ML Research Press; 2022:18741-18753.","ieee":"P. Rolland <i>et al.</i>, “Score matching enables causal discovery of nonlinear additive noise  models,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.","chicago":"Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” In <i>Proceedings of the 39th International Conference on Machine Learning</i>, 162:18741–53. ML Research Press, 2022.","ista":"Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello F. 2022. Score matching enables causal discovery of nonlinear additive noise  models. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 162, 18741–18753."},"date_updated":"2023-09-11T10:14:20Z","author":[{"full_name":"Rolland, Paul","first_name":"Paul","last_name":"Rolland"},{"full_name":"Cevher, Volkan","last_name":"Cevher","first_name":"Volkan"},{"full_name":"Kleindessner, Matthäus","first_name":"Matthäus","last_name":"Kleindessner"},{"full_name":"Russel, Chris","last_name":"Russel","first_name":"Chris"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"first_name":"Dominik","last_name":"Janzing","full_name":"Janzing, Dominik"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello","first_name":"Francesco"}],"day":"22","publication":"Proceedings of the 39th International Conference on Machine Learning","oa":1,"publisher":"ML Research Press","department":[{"_id":"FrLo"}],"month":"07","main_file_link":[{"url":"https://arxiv.org/abs/2203.04413","open_access":"1"}],"arxiv":1,"conference":{"location":"Baltimore, MD, United States","start_date":"2022-07-17","name":"International Conference on Machine Learning","end_date":"2022-07-23"},"page":"18741-18753","date_published":"2022-07-22T00:00:00Z","language":[{"iso":"eng"}],"status":"public","intvolume":"       162","alternative_title":["PMLR"],"extern":"1","type":"conference","volume":162,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"arxiv":1,"conference":{"start_date":"2022-04-25","location":"Virtual","name":"ICLR: International Conference on Learning Representations","end_date":"2022-04-29"},"date_created":"2023-08-22T14:00:50Z","year":"2022","_id":"14172","department":[{"_id":"FrLo"}],"publication_status":"published","month":"04","abstract":[{"lang":"eng","text":"An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models\r\nthat learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards\r\nmore realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factoris out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate\r\ngeneralization."}],"quality_controlled":"1","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2107.08221","open_access":"1"}],"oa_version":"Preprint","article_processing_charge":"No","date_updated":"2023-09-11T09:40:52Z","author":[{"full_name":"Schott, Lukas","last_name":"Schott","first_name":"Lukas"},{"full_name":"Kügelgen, Julius von","first_name":"Julius von","last_name":"Kügelgen"},{"full_name":"Träuble, Frederik","last_name":"Träuble","first_name":"Frederik"},{"full_name":"Gehler, Peter","last_name":"Gehler","first_name":"Peter"},{"first_name":"Chris","last_name":"Russell","full_name":"Russell, Chris"},{"first_name":"Matthias","last_name":"Bethge","full_name":"Bethge, Matthias"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello"},{"full_name":"Brendel, Wieland","last_name":"Brendel","first_name":"Wieland"}],"type":"conference","citation":{"short":"L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on Learning Representations, 2022.","chicago":"Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” In <i>10th International Conference on Learning Representations</i>, 2022.","ista":"Schott L, Kügelgen J von, Träuble F, Gehler P, Russell C, Bethge M, Schölkopf B, Locatello F, Brendel W. 2022. Visual representation learning does not generalize strongly within the  same domain. 10th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","mla":"Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” <i>10th International Conference on Learning Representations</i>, 2022.","apa":"Schott, L., Kügelgen, J. von, Träuble, F., Gehler, P., Russell, C., Bethge, M., … Brendel, W. (2022). Visual representation learning does not generalize strongly within the  same domain. In <i>10th International Conference on Learning Representations</i>. Virtual.","ama":"Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning does not generalize strongly within the  same domain. In: <i>10th International Conference on Learning Representations</i>. ; 2022.","ieee":"L. Schott <i>et al.</i>, “Visual representation learning does not generalize strongly within the  same domain,” in <i>10th International Conference on Learning Representations</i>, Virtual, 2022."},"day":"25","publication":"10th International Conference on Learning Representations","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_published":"2022-04-25T00:00:00Z","language":[{"iso":"eng"}],"title":"Visual representation learning does not generalize strongly within the  same domain","status":"public","external_id":{"arxiv":["2107.08221"]},"extern":"1"}]