[{"type":"conference","acknowledgement":" Monika Henzinger: This project has received funding from the European Research Council\r\n(ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant\r\nagreement No. 101019564 “The Design of Modern Fully Dynamic Data Structures (MoDynStruct)” and from the Austrian Science Fund (FWF) project “Static and Dynamic Hierarchical Graph Decompositions”, I 5982-N, and project “Fast Algorithms for a Reactive Network Layer (ReactNet)”, P 33775-N, with additional funding from the netidee SCIENCE Stiftung, 2020–2024. Jan Vondrák: Supported by NSF Award 2127781.","conference":{"name":"ICALP: Automata, Languages and Programming","start_date":"2023-07-10","location":"Paderborn, Germany","end_date":"2023-07-14"},"file":[{"file_size":930943,"file_id":"14090","relation":"main_file","content_type":"application/pdf","checksum":"a5eef225014e003efbfbe4830fdd23cb","file_name":"2023_LIPIcsICALP_HenzingerM.pdf","date_created":"2023-08-21T07:04:36Z","success":1,"date_updated":"2023-08-21T07:04:36Z","access_level":"open_access","creator":"dernst"}],"alternative_title":["LIPIcs"],"publication":"50th International Colloquium on Automata, Languages, and Programming","citation":{"ista":"Henzinger M, Liu P, Vondrák J, Zheng DW. 2023. Faster submodular maximization for several classes of matroids. 50th International Colloquium on Automata, Languages, and Programming. ICALP: Automata, Languages and Programming, LIPIcs, vol. 261, 74.","mla":"Henzinger, Monika, et al. “Faster Submodular Maximization for Several Classes of Matroids.” <i>50th International Colloquium on Automata, Languages, and Programming</i>, vol. 261, 74, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">10.4230/LIPIcs.ICALP.2023.74</a>.","ama":"Henzinger M, Liu P, Vondrák J, Zheng DW. Faster submodular maximization for several classes of matroids. In: <i>50th International Colloquium on Automata, Languages, and Programming</i>. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:<a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">10.4230/LIPIcs.ICALP.2023.74</a>","ieee":"M. Henzinger, P. Liu, J. Vondrák, and D. W. Zheng, “Faster submodular maximization for several classes of matroids,” in <i>50th International Colloquium on Automata, Languages, and Programming</i>, Paderborn, Germany, 2023, vol. 261.","short":"M. Henzinger, P. Liu, J. Vondrák, D.W. Zheng, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.","chicago":"Henzinger, Monika, Paul Liu, Jan Vondrák, and Da Wei Zheng. “Faster Submodular Maximization for Several Classes of Matroids.” In <i>50th International Colloquium on Automata, Languages, and Programming</i>, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">https://doi.org/10.4230/LIPIcs.ICALP.2023.74</a>.","apa":"Henzinger, M., Liu, P., Vondrák, J., &#38; Zheng, D. W. (2023). Faster submodular maximization for several classes of matroids. In <i>50th International Colloquium on Automata, Languages, and Programming</i> (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.ICALP.2023.74\">https://doi.org/10.4230/LIPIcs.ICALP.2023.74</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ec_funded":1,"arxiv":1,"corr_author":"1","publication_identifier":{"isbn":["9783959772785"],"issn":["1868-8969"]},"quality_controlled":"1","abstract":[{"text":"The maximization of submodular functions have found widespread application in areas such as machine learning, combinatorial optimization, and economics, where practitioners often wish to enforce various constraints; the matroid constraint has been investigated extensively due to its algorithmic properties and expressive power. Though tight approximation algorithms for general matroid constraints exist in theory, the running times of such algorithms typically scale quadratically, and are not practical for truly large scale settings. Recent progress has focused on fast algorithms for important classes of matroids given in explicit form. Currently, nearly-linear time algorithms only exist for graphic and partition matroids [Alina Ene and Huy L. Nguyen, 2019]. In this work, we develop algorithms for monotone submodular maximization constrained by graphic, transversal matroids, or laminar matroids in time near-linear in the size of their representation. Our algorithms achieve an optimal approximation of 1-1/e-ε and both generalize and accelerate the results of Ene and Nguyen [Alina Ene and Huy L. Nguyen, 2019]. In fact, the running time of our algorithm cannot be improved within the fast continuous greedy framework of Badanidiyuru and Vondrák [Ashwinkumar Badanidiyuru and Jan Vondrák, 2014].\r\nTo achieve near-linear running time, we make use of dynamic data structures that maintain bases with approximate maximum cardinality and weight under certain element updates. These data structures need to support a weight decrease operation and a novel Freeze operation that allows the algorithm to freeze elements (i.e. force to be contained) in its basis regardless of future data structure operations. For the laminar matroid, we present a new dynamic data structure using the top tree interface of Alstrup, Holm, de Lichtenberg, and Thorup [Stephen Alstrup et al., 2005] that maintains the maximum weight basis under insertions and deletions of elements in O(log n) time. This data structure needs to support certain subtree query and path update operations that are performed every insertion and deletion that are non-trivial to handle in conjunction. For the transversal matroid the Freeze operation corresponds to requiring the data structure to keep a certain set S of vertices matched, a property that we call S-stability. While there is a large body of work on dynamic matching algorithms, none are S-stable and maintain an approximate maximum weight matching under vertex updates. We give the first such algorithm for bipartite graphs with total running time linear (up to log factors) in the number of edges.","lang":"eng"}],"title":"Faster submodular maximization for several classes of matroids","publication_status":"published","project":[{"grant_number":"101019564","name":"The design and evaluation of modern fully dynamic data structures","_id":"bd9ca328-d553-11ed-ba76-dc4f890cfe62","call_identifier":"H2020"},{"_id":"bda196b2-d553-11ed-ba76-8e8ee6c21103","name":"Static and Dynamic Hierarchical Graph Decompositions","grant_number":"I05982"},{"grant_number":"P33775","_id":"bd9e3a2e-d553-11ed-ba76-8aa684ce17fe","name":"Fast Algorithms for a Reactive Network Layer"}],"date_updated":"2025-07-10T11:50:45Z","external_id":{"arxiv":["2305.00122"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_published":"2023-07-01T00:00:00Z","article_processing_charge":"Yes","has_accepted_license":"1","department":[{"_id":"MoHe"}],"publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","day":"01","license":"https://creativecommons.org/licenses/by/4.0/","year":"2023","language":[{"iso":"eng"}],"ddc":["000"],"_id":"14086","file_date_updated":"2023-08-21T07:04:36Z","oa_version":"Published Version","status":"public","article_number":"74","oa":1,"month":"07","author":[{"orcid":"0000-0002-5008-6530","last_name":"Henzinger","full_name":"Henzinger, Monika H","first_name":"Monika H","id":"540c9bbd-f2de-11ec-812d-d04a5be85630"},{"last_name":"Liu","full_name":"Liu, Paul","first_name":"Paul"},{"full_name":"Vondrák, Jan","last_name":"Vondrák","first_name":"Jan"},{"full_name":"Zheng, Da Wei","last_name":"Zheng","first_name":"Da Wei"}],"volume":261,"date_created":"2023-08-20T22:01:14Z","doi":"10.4230/LIPIcs.ICALP.2023.74","scopus_import":"1","intvolume":"       261"},{"author":[{"first_name":"Jonas","last_name":"Rønning","full_name":"Rønning, Jonas"},{"last_name":"Renaud","full_name":"Renaud, Julian B","first_name":"Julian B","id":"7af6767d-14eb-11ed-b536-a32449ae867c"},{"first_name":"Amin","full_name":"Doostmohammadi, Amin","last_name":"Doostmohammadi"},{"last_name":"Angheluta","full_name":"Angheluta, Luiza","first_name":"Luiza"}],"volume":39,"date_created":"2023-08-20T22:01:15Z","doi":"10.1039/d3sm00316g","scopus_import":"1","intvolume":"        39","_id":"14087","file_date_updated":"2024-01-30T12:48:24Z","ddc":["540"],"status":"public","oa_version":"Published Version","oa":1,"month":"09","page":"7513-7527","year":"2023","language":[{"iso":"eng"}],"article_processing_charge":"Yes (in subscription journal)","has_accepted_license":"1","department":[{"_id":"GradSch"}],"article_type":"original","publisher":"Royal Society of Chemistry","day":"01","quality_controlled":"1","abstract":[{"lang":"eng","text":"Polar active matter of self-propelled particles sustain spontaneous flows through the full-integer topological defects. We study theoretically the incompressible flow profiles around ±1 defects induced by polar and dipolar active forces. We show that dipolar forces induce vortical flows around the +1 defect, while the flow around the −1 defect has an 8-fold rotational symmetry. The vortical flow changes its chirality near the +1 defect core in the absence of the friction with a substrate. We show analytically that the flow induced by polar active forces is vortical near the +1 defect and is 4-fold symmetric near the −1 defect, while it becomes uniform in the far-field. For a pair of oppositely charged defects, this polar flow contributes to a mutual interaction force that depends only on the orientation of the defect pair relative to the background polarization, and that enhances defect pair annihilation. This is in contradiction with the effect of dipolar active forces which decay inversely proportional with the defect separation distance. As such, our analyses reveals a long-ranged mechanism for the pairwise interaction between topological defects in polar active matter."}],"publication_status":"published","title":"Spontaneous flows and dynamics of full-integer topological defects in polar active matter","date_updated":"2025-04-23T13:03:12Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"arxiv":["2303.07063"],"pmid":["37493084"],"isi":["001035766100001"]},"isi":1,"date_published":"2023-09-01T00:00:00Z","arxiv":1,"publication_identifier":{"eissn":["1744-6848"],"issn":["1744-683X"]},"publication":"Soft Matter","citation":{"mla":"Rønning, Jonas, et al. “Spontaneous Flows and Dynamics of Full-Integer Topological Defects in Polar Active Matter.” <i>Soft Matter</i>, vol. 39, Royal Society of Chemistry, 2023, pp. 7513–27, doi:<a href=\"https://doi.org/10.1039/d3sm00316g\">10.1039/d3sm00316g</a>.","ista":"Rønning J, Renaud JB, Doostmohammadi A, Angheluta L. 2023. Spontaneous flows and dynamics of full-integer topological defects in polar active matter. Soft Matter. 39, 7513–7527.","ieee":"J. Rønning, J. B. Renaud, A. Doostmohammadi, and L. Angheluta, “Spontaneous flows and dynamics of full-integer topological defects in polar active matter,” <i>Soft Matter</i>, vol. 39. Royal Society of Chemistry, pp. 7513–7527, 2023.","ama":"Rønning J, Renaud JB, Doostmohammadi A, Angheluta L. Spontaneous flows and dynamics of full-integer topological defects in polar active matter. <i>Soft Matter</i>. 2023;39:7513-7527. doi:<a href=\"https://doi.org/10.1039/d3sm00316g\">10.1039/d3sm00316g</a>","short":"J. Rønning, J.B. Renaud, A. Doostmohammadi, L. Angheluta, Soft Matter 39 (2023) 7513–7527.","chicago":"Rønning, Jonas, Julian B Renaud, Amin Doostmohammadi, and Luiza Angheluta. “Spontaneous Flows and Dynamics of Full-Integer Topological Defects in Polar Active Matter.” <i>Soft Matter</i>. Royal Society of Chemistry, 2023. <a href=\"https://doi.org/10.1039/d3sm00316g\">https://doi.org/10.1039/d3sm00316g</a>.","apa":"Rønning, J., Renaud, J. B., Doostmohammadi, A., &#38; Angheluta, L. (2023). Spontaneous flows and dynamics of full-integer topological defects in polar active matter. <i>Soft Matter</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d3sm00316g\">https://doi.org/10.1039/d3sm00316g</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","pmid":1,"acknowledgement":"J. Rø and L. A. acknowledge support from the Research Council of Norway through the Center of Excellence funding scheme, Project No. 262644 (PoreLab). A. D. acknowledges funding from the Novo Nordisk Foundation (grant No. NNF18SA0035142 and NERD grant No. NNF21OC0068687), Villum Fonden Grant no. 29476, and the European Union via the ERC-Starting Grant PhysCoMeT. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.","type":"journal_article","file":[{"file_name":"2023_SoftMatter_Ronning.pdf","date_created":"2024-01-30T12:48:24Z","date_updated":"2024-01-30T12:48:24Z","success":1,"creator":"dernst","access_level":"open_access","file_size":7660662,"file_id":"14908","relation":"main_file","content_type":"application/pdf","checksum":"b936747170d0b708172b518078c4081a"}]},{"title":"TeST: Test-time Self-Training under distribution shift","publication_status":"published","date_updated":"2023-09-06T10:26:56Z","date_published":"2023-02-06T00:00:00Z","external_id":{"arxiv":["2209.11459"]},"abstract":[{"text":"Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline testtime adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms [4, 43], while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks:\r\nobject detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new stateof-the art for test-time domain adaptation algorithms. ","lang":"eng"}],"quality_controlled":"1","publication_identifier":{"eissn":["2642-9381"],"isbn":["9781665493475"]},"arxiv":1,"citation":{"ieee":"S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>, Waikoloa, HI, United States, 2023.","ama":"Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under distribution shift. In: <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Institute of Electrical and Electronics Engineers; 2023. doi:<a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">10.1109/wacv56688.2023.00278</a>","ista":"Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision.","mla":"Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.” <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>, Institute of Electrical and Electronics Engineers, 2023, doi:<a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">10.1109/wacv56688.2023.00278</a>.","apa":"Sinha, S., Gehler, P., Locatello, F., &#38; Schiele, B. (2023). TeST: Test-time Self-Training under distribution shift. In <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Waikoloa, HI, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">https://doi.org/10.1109/wacv56688.2023.00278</a>","chicago":"Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele. “TeST: Test-Time Self-Training under Distribution Shift.” In <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>. Institute of Electrical and Electronics Engineers, 2023. <a href=\"https://doi.org/10.1109/wacv56688.2023.00278\">https://doi.org/10.1109/wacv56688.2023.00278</a>.","short":"S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision","type":"conference","conference":{"name":"WACV: Winter Conference on Applications of Computer Vision","end_date":"2023-01-07","start_date":"2023-01-02","location":"Waikoloa, HI, United States"},"doi":"10.1109/wacv56688.2023.00278","extern":"1","scopus_import":"1","author":[{"first_name":"Samarth","last_name":"Sinha","full_name":"Sinha, Samarth"},{"first_name":"Peter","full_name":"Gehler, Peter","last_name":"Gehler"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"full_name":"Schiele, Bernt","last_name":"Schiele","first_name":"Bernt"}],"main_file_link":[{"url":"https://arxiv.org/abs/2209.11459","open_access":"1"}],"date_created":"2023-08-21T12:11:38Z","oa":1,"month":"02","_id":"14105","oa_version":"Preprint","status":"public","year":"2023","language":[{"iso":"eng"}],"publisher":"Institute of Electrical and Electronics Engineers","day":"06","article_processing_charge":"No","department":[{"_id":"FrLo"}]},{"file":[{"date_created":"2023-08-23T10:59:15Z","file_name":"2023_MathPhysics_Lampart.pdf","success":1,"date_updated":"2023-08-23T10:59:15Z","creator":"dernst","access_level":"open_access","file_id":"14225","file_size":317026,"content_type":"application/pdf","relation":"main_file","checksum":"f0941cc66cb3ed06a12ca4b7e356cfd6"}],"acknowledgement":"D.M. and K.M. thank Robert Seiringer for helpful discussions. Open access funding provided by Institute of Science and Technology (IST Austria). Financial support from the Agence Nationale de la Recherche (ANR) through the projects ANR-17-CE40-0016, ANR-17-CE40-0007-01, ANR-17-EURE-0002 (J.L.) and from the European Union’s Horizon 2020 research and innovation programme under the Maria Skłodowska-Curie grant agreement No. 665386 (K.M.) is gratefully acknowledged.","type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"Lampart, J., Mitrouskas, D. J., &#38; Mysliwy, K. (2023). On the global minimum of the energy–momentum relation for the polaron. <i>Mathematical Physics, Analysis and Geometry</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s11040-023-09460-x\">https://doi.org/10.1007/s11040-023-09460-x</a>","chicago":"Lampart, Jonas, David Johannes Mitrouskas, and Krzysztof Mysliwy. “On the Global Minimum of the Energy–Momentum Relation for the Polaron.” <i>Mathematical Physics, Analysis and Geometry</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1007/s11040-023-09460-x\">https://doi.org/10.1007/s11040-023-09460-x</a>.","short":"J. Lampart, D.J. Mitrouskas, K. Mysliwy, Mathematical Physics, Analysis and Geometry 26 (2023).","ama":"Lampart J, Mitrouskas DJ, Mysliwy K. On the global minimum of the energy–momentum relation for the polaron. <i>Mathematical Physics, Analysis and Geometry</i>. 2023;26(3). doi:<a href=\"https://doi.org/10.1007/s11040-023-09460-x\">10.1007/s11040-023-09460-x</a>","ieee":"J. Lampart, D. J. Mitrouskas, and K. Mysliwy, “On the global minimum of the energy–momentum relation for the polaron,” <i>Mathematical Physics, Analysis and Geometry</i>, vol. 26, no. 3. Springer Nature, 2023.","mla":"Lampart, Jonas, et al. “On the Global Minimum of the Energy–Momentum Relation for the Polaron.” <i>Mathematical Physics, Analysis and Geometry</i>, vol. 26, no. 3, 17, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1007/s11040-023-09460-x\">10.1007/s11040-023-09460-x</a>.","ista":"Lampart J, Mitrouskas DJ, Mysliwy K. 2023. On the global minimum of the energy–momentum relation for the polaron. Mathematical Physics, Analysis and Geometry. 26(3), 17."},"publication":"Mathematical Physics, Analysis and Geometry","publication_identifier":{"eissn":["1572-9656"],"issn":["1385-0172"]},"corr_author":"1","arxiv":1,"date_published":"2023-07-26T00:00:00Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"isi":1,"external_id":{"isi":["001032992600001"],"arxiv":["2206.14708"]},"date_updated":"2024-10-09T21:06:41Z","title":"On the global minimum of the energy–momentum relation for the polaron","publication_status":"published","abstract":[{"lang":"eng","text":"For the Fröhlich model of the large polaron, we prove that the ground state energy as a function of the total momentum has a unique global minimum at momentum zero. This implies the non-existence of a ground state of the translation invariant Fröhlich Hamiltonian and thus excludes the possibility of a localization transition at finite coupling."}],"quality_controlled":"1","issue":"3","day":"26","publisher":"Springer Nature","article_type":"original","department":[{"_id":"RoSe"}],"has_accepted_license":"1","article_processing_charge":"Yes (via OA deal)","language":[{"iso":"eng"}],"year":"2023","keyword":["Geometry and Topology","Mathematical Physics"],"month":"07","oa":1,"article_number":"17","oa_version":"Published Version","status":"public","_id":"14192","file_date_updated":"2023-08-23T10:59:15Z","ddc":["510"],"intvolume":"        26","scopus_import":"1","doi":"10.1007/s11040-023-09460-x","date_created":"2023-08-22T14:09:47Z","volume":26,"author":[{"full_name":"Lampart, Jonas","last_name":"Lampart","first_name":"Jonas"},{"last_name":"Mitrouskas","full_name":"Mitrouskas, David Johannes","first_name":"David Johannes","id":"cbddacee-2b11-11eb-a02e-a2e14d04e52d"},{"last_name":"Mysliwy","full_name":"Mysliwy, Krzysztof","id":"316457FC-F248-11E8-B48F-1D18A9856A87","first_name":"Krzysztof"}]},{"publication":"arXiv","citation":{"mla":"Löwe, Sindy, et al. “Rotating Features for Object Discovery.” <i>ArXiv</i>, 2306.00600, doi:<a href=\"https://doi.org/10.48550/arXiv.2306.00600\">10.48550/arXiv.2306.00600</a>.","ista":"Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery. arXiv, 2306.00600.","ieee":"S. Löwe, P. Lippe, F. Locatello, and M. Welling, “Rotating features for object discovery,” <i>arXiv</i>. .","ama":"Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2306.00600\">10.48550/arXiv.2306.00600</a>","short":"S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).","chicago":"Löwe, Sindy, Phillip Lippe, Francesco Locatello, and Max Welling. “Rotating Features for Object Discovery.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2306.00600\">https://doi.org/10.48550/arXiv.2306.00600</a>.","apa":"Löwe, S., Lippe, P., Locatello, F., &#38; Welling, M. (n.d.). Rotating features for object discovery. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2306.00600\">https://doi.org/10.48550/arXiv.2306.00600</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","language":[{"iso":"eng"}],"article_processing_charge":"No","type":"preprint","department":[{"_id":"FrLo"}],"day":"01","author":[{"last_name":"Löwe","full_name":"Löwe, Sindy","first_name":"Sindy"},{"first_name":"Phillip","last_name":"Lippe","full_name":"Lippe, Phillip"},{"last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"},{"full_name":"Welling, Max","last_name":"Welling","first_name":"Max"}],"abstract":[{"lang":"eng","text":"The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate. Most machine learning efforts addressing this issue in an unsupervised setting have focused on slot-based methods, which may be limiting due to their discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder was proposed as an alternative that learns continuous and distributed object-centric representations. However, it is only applicable to simple toy data. In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations. Additionally, we show the applicability of our approach to pre-trained features. Together, these advancements enable us to scale distributed object-centric representations from simple toy to real-world data. We believe this work advances a new paradigm for addressing the binding problem in machine learning and has the potential to inspire further innovation in the field."}],"main_file_link":[{"url":"https://arxiv.org/abs/2306.00600","open_access":"1"}],"date_created":"2023-08-22T14:18:00Z","doi":"10.48550/arXiv.2306.00600","publication_status":"submitted","title":"Rotating features for object discovery","date_updated":"2024-10-09T21:06:53Z","external_id":{"arxiv":["2306.00600"]},"date_published":"2023-06-01T00:00:00Z","arxiv":1,"_id":"14207","oa_version":"Preprint","status":"public","article_number":"2306.00600","corr_author":"1","oa":1,"month":"06"},{"year":"2023","language":[{"iso":"eng"}],"page":"43105-43128","publisher":"ML Research Press","day":"30","article_processing_charge":"No","department":[{"_id":"FrLo"}],"extern":"1","intvolume":"       202","author":[{"full_name":"Zhu, Zhenyu","last_name":"Zhu","first_name":"Zhenyu"},{"full_name":"Liu, Fanghui","last_name":"Liu","first_name":"Fanghui"},{"first_name":"Grigorios G","full_name":"Chrysos, Grigorios G","last_name":"Chrysos"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"first_name":"Volkan","full_name":"Cevher, Volkan","last_name":"Cevher"}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2305.19377","open_access":"1"}],"volume":202,"date_created":"2023-08-22T14:18:18Z","oa":1,"month":"05","_id":"14208","oa_version":"Preprint","status":"public","citation":{"ista":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting in deep neural networks under lazy training. Proceedings of the 40th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 202, 43105–43128.","mla":"Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy Training.” <i>Proceedings of the 40th International Conference on Machine Learning</i>, vol. 202, ML Research Press, 2023, pp. 43105–28.","ama":"Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: <i>Proceedings of the 40th International Conference on Machine Learning</i>. Vol 202. ML Research Press; 2023:43105-43128.","ieee":"Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in <i>Proceedings of the 40th International Conference on Machine Learning</i>, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128.","short":"Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.","apa":"Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., &#38; Cevher, V. (2023). Benign overfitting in deep neural networks under lazy training. In <i>Proceedings of the 40th International Conference on Machine Learning</i> (Vol. 202, pp. 43105–43128). Honolulu, Hawaii, United States: ML Research Press.","chicago":"Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.” In <i>Proceedings of the 40th International Conference on Machine Learning</i>, 202:43105–28. ML Research Press, 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Proceedings of the 40th International Conference on Machine Learning","alternative_title":["PMLR"],"type":"conference","conference":{"end_date":"2023-07-29","start_date":"2023-07-23","location":"Honolulu, Hawaii, United States","name":"International Conference on Machine Learning"},"title":"Benign overfitting in deep neural networks under lazy training","publication_status":"published","date_updated":"2023-09-13T08:46:46Z","date_published":"2023-05-30T00:00:00Z","external_id":{"arxiv":["2305.19377"]},"quality_controlled":"1","abstract":[{"text":"This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly) zero-training error under the lazy training regime. For this purpose, we unify three interrelated concepts of overparameterization, benign overfitting, and the Lipschitz constant of DNNs. Our results indicate that interpolating with smoother functions leads to better generalization. Furthermore, we investigate the special case where interpolating smooth ground-truth functions is performed by DNNs under the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates that the generalization error converges to a constant order that only depends on label noise and initialization noise, which theoretically verifies benign overfitting. Our analysis provides a tight lower bound on the normalized margin under non-smooth activation functions, as well as the minimum eigenvalue of NTK under high-dimensional settings, which has its own interest in learning theory.","lang":"eng"}],"arxiv":1},{"language":[{"iso":"eng"}],"year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"M. F. Burg <i>et al.</i>, “A data augmentation perspective on diffusion models and retrieval,” <i>arXiv</i>. .","ama":"Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>","mla":"Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, 2304.10253, doi:<a href=\"https://doi.org/10.48550/arXiv.2304.10253\">10.48550/arXiv.2304.10253</a>.","ista":"Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253.","chicago":"Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>.","apa":"Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &#38; Russell, C. (n.d.). A data augmentation perspective on diffusion models and retrieval. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2304.10253\">https://doi.org/10.48550/arXiv.2304.10253</a>","short":"M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.)."},"publication":"arXiv","day":"20","department":[{"_id":"FrLo"}],"type":"preprint","article_processing_charge":"No","external_id":{"arxiv":["2304.10253"]},"date_published":"2023-04-20T00:00:00Z","date_updated":"2023-09-13T08:51:56Z","doi":"10.48550/arXiv.2304.10253","title":"A data augmentation perspective on diffusion models and retrieval","extern":"1","publication_status":"submitted","date_created":"2023-08-22T14:18:43Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2304.10253"}],"abstract":[{"text":"Diffusion models excel at generating photorealistic images from text-queries. Naturally, many approaches have been proposed to use these generative abilities to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large noisily supervised, but nonetheless, annotated datasets. It is an open question whether the generalization capabilities of diffusion models beyond using the additional data of the pre-training process for augmentation lead to improved downstream performance. We perform a systematic evaluation of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. While we find that personalizing diffusion models towards the target data outperforms simpler prompting strategies, we also show that using the training data of the diffusion model alone, via a simple nearest neighbor retrieval procedure, leads to even stronger downstream performance. Overall, our study probes the limitations of diffusion models for data augmentation but also highlights its potential in generating new training data to improve performance on simple downstream vision tasks.","lang":"eng"}],"author":[{"first_name":"Max F.","last_name":"Burg","full_name":"Burg, Max F."},{"first_name":"Florian","full_name":"Wenzel, Florian","last_name":"Wenzel"},{"first_name":"Dominik","last_name":"Zietlow","full_name":"Zietlow, Dominik"},{"first_name":"Max","full_name":"Horn, Max","last_name":"Horn"},{"last_name":"Makansi","full_name":"Makansi, Osama","first_name":"Osama"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"last_name":"Russell","full_name":"Russell, Chris","first_name":"Chris"}],"month":"04","oa":1,"article_number":"2304.10253","status":"public","oa_version":"Preprint","_id":"14209","arxiv":1},{"date_created":"2023-08-22T14:19:03Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2304.07939","open_access":"1"}],"abstract":[{"text":"Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. Assuming each supervised task only depends on an unknown subset of the factors of variation, we disentangle the feature space of a supervised multi-task model, with features activating sparsely across different tasks and information being shared as appropriate. Importantly, we never directly observe the factors of variations but establish that access to multiple tasks is sufficient for identifiability under sufficiency and minimality assumptions. We validate our approach on six real world distribution shift benchmarks, and different data modalities (images, text), demonstrating how disentangled representations can be transferred to real settings.","lang":"eng"}],"author":[{"full_name":"Fumero, Marco","last_name":"Fumero","first_name":"Marco"},{"last_name":"Wenzel","full_name":"Wenzel, Florian","first_name":"Florian"},{"full_name":"Zancato, Luca","last_name":"Zancato","first_name":"Luca"},{"first_name":"Alessandro","last_name":"Achille","full_name":"Achille, Alessandro"},{"first_name":"Emanuele","last_name":"Rodolà","full_name":"Rodolà, Emanuele"},{"full_name":"Soatto, Stefano","last_name":"Soatto","first_name":"Stefano"},{"last_name":"Schölkopf","full_name":"Schölkopf, Bernhard","first_name":"Bernhard"},{"last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco"}],"date_published":"2023-04-17T00:00:00Z","external_id":{"arxiv":["2304.07939"]},"date_updated":"2024-10-09T21:06:54Z","doi":"10.48550/arXiv.2304.07939","publication_status":"submitted","title":"Leveraging sparse and shared feature activations for disentangled representation learning","article_number":"2304.07939","status":"public","oa_version":"Preprint","_id":"14210","arxiv":1,"month":"04","oa":1,"corr_author":"1","publication":"arXiv","language":[{"iso":"eng"}],"year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).","apa":"Fumero, M., Wenzel, F., Zancato, L., Achille, A., Rodolà, E., Soatto, S., … Locatello, F. (n.d.). Leveraging sparse and shared feature activations for disentangled representation learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2304.07939\">https://doi.org/10.48550/arXiv.2304.07939</a>","chicago":"Fumero, Marco, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2304.07939\">https://doi.org/10.48550/arXiv.2304.07939</a>.","ista":"Fumero M, Wenzel F, Zancato L, Achille A, Rodolà E, Soatto S, Schölkopf B, Locatello F. Leveraging sparse and shared feature activations for disentangled representation learning. arXiv, 2304.07939.","mla":"Fumero, Marco, et al. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.” <i>ArXiv</i>, 2304.07939, doi:<a href=\"https://doi.org/10.48550/arXiv.2304.07939\">10.48550/arXiv.2304.07939</a>.","ama":"Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature activations for disentangled representation learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2304.07939\">10.48550/arXiv.2304.07939</a>","ieee":"M. Fumero <i>et al.</i>, “Leveraging sparse and shared feature activations for disentangled representation learning,” <i>arXiv</i>. ."},"department":[{"_id":"FrLo"}],"type":"preprint","article_processing_charge":"No","day":"17"},{"month":"04","oa":1,"oa_version":"Preprint","status":"public","_id":"14211","arxiv":1,"date_published":"2023-04-01T00:00:00Z","external_id":{"arxiv":["2304.03265"]},"date_updated":"2024-10-14T12:30:04Z","scopus_import":"1","extern":"1","title":"Causal discovery with score matching on additive models with arbitrary noise","publication_status":"published","date_created":"2023-08-22T14:19:21Z","quality_controlled":"1","abstract":[{"text":"Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian noise assumption on additive non-linear models, which is common to many causal discovery approaches. In this paper we show the shortcomings of inference under this hypothesis, analyzing the risk of edge inversion under violation of Gaussianity of the noise terms. Then, we propose a novel method for inferring the topological ordering of the variables in the causal graph, from data generated according to an additive non-linear model with a generic noise distribution. This leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm with a minimal set of assumptions and state of the art performance, experimentally benchmarked on synthetic data.","lang":"eng"}],"main_file_link":[{"url":"https://arxiv.org/abs/2304.03265","open_access":"1"}],"author":[{"first_name":"Francesco","full_name":"Montagna, Francesco","last_name":"Montagna"},{"first_name":"Nicoletta","last_name":"Noceti","full_name":"Noceti, Nicoletta"},{"full_name":"Rosasco, Lorenzo","last_name":"Rosasco","first_name":"Lorenzo"},{"first_name":"Kun","full_name":"Zhang, Kun","last_name":"Zhang"},{"orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","last_name":"Locatello","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4"}],"day":"01","department":[{"_id":"FrLo"}],"conference":{"end_date":"2023-04-14","location":"Tübingen, Germany","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"type":"conference","article_processing_charge":"No","language":[{"iso":"eng"}],"year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","mla":"Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery with score matching on additive models with arbitrary noise. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Causal discovery with score matching on additive models with arbitrary noise. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023."},"publication":"2nd Conference on Causal Learning and Reasoning"},{"article_processing_charge":"No","type":"conference","conference":{"end_date":"2023-04-14","location":"Tübingen, Germany","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"department":[{"_id":"FrLo"}],"day":"01","publication":"2nd Conference on Causal Learning and Reasoning","citation":{"short":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","chicago":"Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Montagna, F., Noceti, N., Rosasco, L., Zhang, K., &#38; Locatello, F. (2023). Scalable causal discovery with score matching. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","ista":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal discovery with score matching. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.","mla":"Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ama":"Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ieee":"F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2023","language":[{"iso":"eng"}],"_id":"14212","arxiv":1,"oa_version":"Preprint","status":"public","oa":1,"month":"04","author":[{"first_name":"Francesco","full_name":"Montagna, Francesco","last_name":"Montagna"},{"first_name":"Nicoletta","full_name":"Noceti, Nicoletta","last_name":"Noceti"},{"last_name":"Rosasco","full_name":"Rosasco, Lorenzo","first_name":"Lorenzo"},{"full_name":"Zhang, Kun","last_name":"Zhang","first_name":"Kun"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"}],"quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/2304.03382","open_access":"1"}],"abstract":[{"lang":"eng","text":"This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function ∇logp(X), we extend the work of Rolland et al. (2022) that only recovers the topological order from the score and requires an expensive pruning step removing spurious edges among those admitted by the ordering. Our analysis leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces the complexity of the pruning by a factor proportional to the graph size. In practice, DAS achieves competitive accuracy with current state-of-the-art while being over an order of magnitude faster. Overall, our approach enables principled and scalable causal discovery, significantly lowering the compute bar."}],"date_created":"2023-08-22T14:19:40Z","publication_status":"published","extern":"1","title":"Scalable causal discovery with score matching","scopus_import":"1","date_updated":"2024-10-14T12:30:15Z","date_published":"2023-04-01T00:00:00Z","external_id":{"arxiv":["2304.03382"]}},{"publication":"2nd Conference on Causal Learning and Reasoning","language":[{"iso":"eng"}],"year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","apa":"Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &#38; Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric causal representation learning. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","short":"Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","ama":"Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023.","ieee":"Y. Liu <i>et al.</i>, “Causal triplet: An open challenge for intervention-centric causal representation learning,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","mla":"Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023. Causal triplet: An open challenge for intervention-centric causal representation learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning."},"conference":{"name":"CLeaR: Conference on Causal Learning and Reasoning","end_date":"2023-04-14","location":"Tübingen, Germany","start_date":"2023-04-11"},"department":[{"_id":"FrLo"}],"type":"conference","article_processing_charge":"No","day":"12","date_created":"2023-08-22T14:20:18Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2301.05169"}],"quality_controlled":"1","abstract":[{"text":"Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work.","lang":"eng"}],"author":[{"first_name":"Yuejiang","full_name":"Liu, Yuejiang","last_name":"Liu"},{"full_name":"Alahi, Alexandre","last_name":"Alahi","first_name":"Alexandre"},{"full_name":"Russell, Chris","last_name":"Russell","first_name":"Chris"},{"last_name":"Horn","full_name":"Horn, Max","first_name":"Max"},{"full_name":"Zietlow, Dominik","last_name":"Zietlow","first_name":"Dominik"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683"}],"date_published":"2023-04-12T00:00:00Z","external_id":{"arxiv":["2301.05169"]},"date_updated":"2024-10-14T12:30:42Z","extern":"1","title":"Causal triplet: An open challenge for intervention-centric causal representation learning","publication_status":"published","oa_version":"Preprint","status":"public","arxiv":1,"_id":"14214","month":"04","oa":1},{"related_material":{"link":[{"url":"https://github.com/noranta4/ASIF","relation":"software"}]},"date_created":"2023-08-22T14:22:04Z","volume":36,"author":[{"first_name":"Antonio","full_name":"Norelli, Antonio","last_name":"Norelli"},{"first_name":"Marco","last_name":"Fumero","full_name":"Fumero, Marco"},{"first_name":"Valentino","last_name":"Maiorca","full_name":"Maiorca, Valentino"},{"full_name":"Moschella, Luca","last_name":"Moschella","first_name":"Luca"},{"full_name":"Rodolà, Emanuele","last_name":"Rodolà","first_name":"Emanuele"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","full_name":"Locatello, Francesco","last_name":"Locatello"}],"intvolume":"        36","status":"public","oa_version":"Preprint","file_date_updated":"2025-02-04T12:16:13Z","_id":"14216","ddc":["000"],"month":"10","oa":1,"page":"15303-15319","language":[{"iso":"eng"}],"year":"2023","OA_type":"green","department":[{"_id":"FrLo"}],"has_accepted_license":"1","article_processing_charge":"No","day":"04","publisher":"Neural Information Processing Systems Foundation","abstract":[{"text":"CLIP proved that aligning visual and language spaces is key to solving many vision tasks without explicit training, but required to train image and text encoders from scratch on a huge dataset. LiT improved this by only training the text encoder and using a pre-trained vision network. In this paper, we show that a common space can be created without any training at all, using single-domain encoders (trained with or without supervision) and a much smaller amount of image-text pairs. Furthermore, our model has unique properties. Most notably, deploying a new version with updated training samples can be done in a matter of seconds. Additionally, the representations in the common space are easily interpretable as every dimension corresponds to the similarity of the input to a unique entry in the multimodal dataset. Experiments on standard zero-shot visual benchmarks demonstrate the typical transfer ability of image-text models. Overall, our method represents a simple yet surprisingly strong baseline for foundation multi-modal models, raising important questions on their data efficiency and on the role of retrieval in machine learning.","lang":"eng"}],"quality_controlled":"1","date_published":"2023-10-04T00:00:00Z","external_id":{"arxiv":["2210.01738"]},"date_updated":"2025-05-14T11:28:52Z","title":"ASIF: Coupled data turns unimodal models to multimodal without training","publication_status":"published","arxiv":1,"publication_identifier":{"isbn":["9781713899921"]},"corr_author":"1","publication":"37th Conference on Neural Information Processing Systems","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Norelli, Antonio, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” In <i>37th Conference on Neural Information Processing Systems</i>, 36:15303–19. Neural Information Processing Systems Foundation, 2023.","apa":"Norelli, A., Fumero, M., Maiorca, V., Moschella, L., Rodolà, E., &#38; Locatello, F. (2023). ASIF: Coupled data turns unimodal models to multimodal without training. In <i>37th Conference on Neural Information Processing Systems</i> (Vol. 36, pp. 15303–15319). New Orleans, LA, United States: Neural Information Processing Systems Foundation.","short":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello, in:, 37th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2023, pp. 15303–15319.","ieee":"A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello, “ASIF: Coupled data turns unimodal models to multimodal without training,” in <i>37th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States, 2023, vol. 36, pp. 15303–15319.","ama":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF: Coupled data turns unimodal models to multimodal without training. In: <i>37th Conference on Neural Information Processing Systems</i>. Vol 36. Neural Information Processing Systems Foundation; 2023:15303-15319.","mla":"Norelli, Antonio, et al. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.” <i>37th Conference on Neural Information Processing Systems</i>, vol. 36, Neural Information Processing Systems Foundation, 2023, pp. 15303–19.","ista":"Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. 2023. ASIF: Coupled data turns unimodal models to multimodal without training. 37th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 36, 15303–15319."},"conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2023-12-14","start_date":"2023-12-12","location":"New Orleans, LA, United States"},"type":"conference","acknowledgement":"AN, MF, and FL partially worked on ASIF when they were at Amazon Web Services in Tübingen,\r\nGermany. This paper is financially supported by the PRIN 2020 project no.2020TA3K9N (LEGO.AI), PNRR MUR project PE0000013-FAIR, and ERC Grant no.802554 (SPECGEO).","alternative_title":["Advances in Neural Information Processing Systems"],"file":[{"date_created":"2025-02-04T12:16:13Z","file_name":"2023_NeurIPS_Fumero.pdf","success":1,"date_updated":"2025-02-04T12:16:13Z","access_level":"open_access","creator":"dernst","file_size":12648978,"file_id":"18994","content_type":"application/pdf","relation":"main_file","checksum":"e51c90300b92d7135050da5c9e3a8015"}]},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. 2023. Relative representations enable zero-shot latent space communication. The 11th International Conference on Learning Representations. International Conference on Machine Learning Representations.","mla":"Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space Communication.” <i>The 11th International Conference on Learning Representations</i>, 2023.","ieee":"L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà, “Relative representations enable zero-shot latent space communication,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ama":"Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative representations enable zero-shot latent space communication. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","short":"L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà, in:, The 11th International Conference on Learning Representations, 2023.","apa":"Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., &#38; Rodolà, E. (2023). Relative representations enable zero-shot latent space communication. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","chicago":"Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent Space Communication.” In <i>The 11th International Conference on Learning Representations</i>, 2023."},"language":[{"iso":"eng"}],"year":"2023","publication":"The 11th International Conference on Learning Representations","day":"01","type":"conference","article_processing_charge":"No","department":[{"_id":"FrLo"}],"conference":{"name":"International Conference on Machine Learning Representations","end_date":"2023-05-05","start_date":"2023-05-01","location":"Kigali, Rwanda"},"date_updated":"2023-09-13T09:44:26Z","publication_status":"published","title":"Relative representations enable zero-shot latent space communication","extern":"1","external_id":{"arxiv":["2209.15430"]},"date_published":"2023-05-01T00:00:00Z","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2209.15430"}],"abstract":[{"text":"Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the random weights initialization, training hyperparameters, or other sources of randomness in the training phase may induce incoherent latent spaces that hinder any form of reuse. Nevertheless, we empirically observe that, under the same data and modeling choices, the angles between the encodings within distinct latent spaces do not change. In this work, we propose the latent similarity between each sample and a fixed set of anchors as an alternative data representation, demonstrating that it can enforce the desired invariances without any additional training. We show how neural architectures can leverage these relative representations to guarantee, in practice, invariance to latent isometries and rescalings, effectively enabling latent space communication: from zero-shot model stitching to latent space comparison between diverse settings. We extensively validate the generalization capability of our approach on different datasets, spanning various modalities (images, text, graphs), tasks (e.g., classification, reconstruction) and architectures (e.g., CNNs, GCNs, transformers).","lang":"eng"}],"quality_controlled":"1","author":[{"full_name":"Moschella, Luca","last_name":"Moschella","first_name":"Luca"},{"full_name":"Maiorca, Valentino","last_name":"Maiorca","first_name":"Valentino"},{"last_name":"Fumero","full_name":"Fumero, Marco","first_name":"Marco"},{"last_name":"Norelli","full_name":"Norelli, Antonio","first_name":"Antonio"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco"},{"last_name":"Rodolà","full_name":"Rodolà, Emanuele","first_name":"Emanuele"}],"date_created":"2023-08-22T14:22:20Z","oa":1,"month":"05","oa_version":"Preprint","status":"public","arxiv":1,"_id":"14217"},{"month":"05","oa":1,"status":"public","oa_version":"Preprint","_id":"14218","arxiv":1,"date_published":"2023-05-10T00:00:00Z","external_id":{"arxiv":["2209.14860"]},"date_updated":"2024-10-14T12:30:54Z","extern":"1","title":"Bridging the gap to real-world object-centric learning","publication_status":"published","date_created":"2023-08-22T14:22:41Z","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2209.14860"}],"abstract":[{"lang":"eng","text":"Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing image-based object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature."}],"author":[{"first_name":"Maximilian","full_name":"Seitzer, Maximilian","last_name":"Seitzer"},{"first_name":"Max","last_name":"Horn","full_name":"Horn, Max"},{"first_name":"Andrii","last_name":"Zadaianchuk","full_name":"Zadaianchuk, Andrii"},{"first_name":"Dominik","full_name":"Zietlow, Dominik","last_name":"Zietlow"},{"full_name":"Xiao, Tianjun","last_name":"Xiao","first_name":"Tianjun"},{"full_name":"Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel","last_name":"Carl-Johann Simon-Gabriel","first_name":"Carl-Johann Simon-Gabriel"},{"first_name":"Tong","full_name":"He, Tong","last_name":"He"},{"full_name":"Zhang, Zheng","last_name":"Zhang","first_name":"Zheng"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"},{"last_name":"Brox","full_name":"Brox, Thomas","first_name":"Thomas"},{"first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683","last_name":"Locatello","full_name":"Locatello, Francesco"}],"day":"10","conference":{"name":"ICLR: International Conference on Learning Representations","end_date":"2023-05-05","start_date":"2023-05-01","location":"Kigali, Rwanda"},"department":[{"_id":"FrLo"}],"type":"conference","article_processing_charge":"No","language":[{"iso":"eng"}],"year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging the Gap to Real-World Object-Centric Learning.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","apa":"Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world object-centric learning. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","short":"M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The 11th International Conference on Learning Representations, 2023.","ieee":"M. Seitzer <i>et al.</i>, “Bridging the gap to real-world object-centric learning,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","ama":"Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric learning. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","mla":"Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric Learning.” <i>The 11th International Conference on Learning Representations</i>, 2023.","ista":"Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap to real-world object-centric learning. The 11th International Conference on Learning Representations. ICLR: International Conference on Learning Representations."},"publication":"The 11th International Conference on Learning Representations"},{"_id":"14219","arxiv":1,"oa_version":"Preprint","status":"public","month":"05","oa":1,"date_created":"2023-08-22T14:22:58Z","author":[{"first_name":"Andrii","last_name":"Zadaianchuk","full_name":"Zadaianchuk, Andrii"},{"last_name":"Kleindessner","full_name":"Kleindessner, Matthaeus","first_name":"Matthaeus"},{"last_name":"Zhu","full_name":"Zhu, Yi","first_name":"Yi"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco","last_name":"Locatello","orcid":"0000-0002-4850-0683"},{"full_name":"Brox, Thomas","last_name":"Brox","first_name":"Thomas"}],"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2207.05027"}],"abstract":[{"text":"In this paper, we show that recent advances in self-supervised feature\r\nlearning enable unsupervised object discovery and semantic segmentation with a\r\nperformance that matches the state of the field on supervised semantic\r\nsegmentation 10 years ago. We propose a methodology based on unsupervised\r\nsaliency masks and self-supervised feature clustering to kickstart object\r\ndiscovery followed by training a semantic segmentation network on pseudo-labels\r\nto bootstrap the system on images with multiple objects. We present results on\r\nPASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers 34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of 19.6 for all 81 categories.","lang":"eng"}],"external_id":{"arxiv":["2207.05027"]},"date_published":"2023-05-01T00:00:00Z","extern":"1","title":"Unsupervised semantic segmentation with self-supervised object-centric representations","publication_status":"published","date_updated":"2023-09-13T11:25:43Z","department":[{"_id":"FrLo"}],"conference":{"location":"Kigali, Rwanda","start_date":"2023-05-01","end_date":"2023-05-05","name":"ICLR: International Conference on Learning Representations"},"article_processing_charge":"No","type":"conference","day":"01","publication":"The 11th International Conference on Learning Representations","year":"2023","language":[{"iso":"eng"}],"citation":{"chicago":"Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” In <i>The 11th International Conference on Learning Representations</i>, 2023.","apa":"Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., &#38; Brox, T. (2023). Unsupervised semantic segmentation with self-supervised object-centric representations. In <i>The 11th International Conference on Learning Representations</i>. Kigali, Rwanda.","short":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The 11th International Conference on Learning Representations, 2023.","ama":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: <i>The 11th International Conference on Learning Representations</i>. ; 2023.","ieee":"A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised semantic segmentation with self-supervised object-centric representations,” in <i>The 11th International Conference on Learning Representations</i>, Kigali, Rwanda, 2023.","mla":"Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” <i>The 11th International Conference on Learning Representations</i>, 2023.","ista":"Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised semantic segmentation with self-supervised object-centric representations. The 11th International Conference on Learning Representations. ICLR: International Conference on Learning Representations."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"date_created":"2023-08-22T14:23:54Z","author":[{"first_name":"Matthias","full_name":"Tangemann, Matthias","last_name":"Tangemann"},{"last_name":"Schneider","full_name":"Schneider, Steffen","first_name":"Steffen"},{"full_name":"Kügelgen, Julius von","last_name":"Kügelgen","first_name":"Julius von"},{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","last_name":"Locatello","full_name":"Locatello, Francesco","orcid":"0000-0002-4850-0683"},{"last_name":"Gehler","full_name":"Gehler, Peter","first_name":"Peter"},{"full_name":"Brox, Thomas","last_name":"Brox","first_name":"Thomas"},{"first_name":"Matthias","full_name":"Kümmerer, Matthias","last_name":"Kümmerer"},{"first_name":"Matthias","last_name":"Bethge","full_name":"Bethge, Matthias"},{"first_name":"Bernhard","full_name":"Schölkopf, Bernhard","last_name":"Schölkopf"}],"quality_controlled":"1","abstract":[{"text":"Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks of moving objects via unsupervised motion segmentation. Second, generative models are trained on the masks of the background and the moving objects, respectively. Third, background and foreground models are combined in a conditional \"dead leaves\" scene model to sample novel scene configurations where occlusions and depth layering arise naturally. To evaluate the individual stages, we introduce the Fishbowl dataset positioned between complex real-world scenes and common object-centric benchmarks of simplistic objects. We show that our approach allows learning generative models that generalize beyond the occlusions present in the input videos, and represent scenes in a modular fashion that allows sampling plausible scenes outside the training distribution by permitting, for instance, object numbers or densities not observed in the training set.","lang":"eng"}],"main_file_link":[{"url":"https://arxiv.org/abs/2110.06562","open_access":"1"}],"external_id":{"arxiv":["2110.06562"]},"date_published":"2023-04-15T00:00:00Z","publication_status":"published","extern":"1","title":"Unsupervised object learning via common fate","date_updated":"2023-09-13T11:31:14Z","article_number":"2110.06562","_id":"14222","arxiv":1,"oa_version":"Preprint","status":"public","month":"04","oa":1,"publication":"2nd Conference on Causal Learning and Reasoning","year":"2023","language":[{"iso":"eng"}],"citation":{"short":"M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T. Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning and Reasoning, 2023.","apa":"Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P., Brox, T., … Schölkopf, B. (2023). Unsupervised object learning via common fate. In <i>2nd Conference on Causal Learning and Reasoning</i>. Tübingen, Germany.","chicago":"Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In <i>2nd Conference on Causal Learning and Reasoning</i>, 2023.","ista":"Tangemann M, Schneider S, Kügelgen J von, Locatello F, Gehler P, Brox T, Kümmerer M, Bethge M, Schölkopf B. 2023. Unsupervised object learning via common fate. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning, 2110.06562.","mla":"Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.” <i>2nd Conference on Causal Learning and Reasoning</i>, 2110.06562, 2023.","ieee":"M. Tangemann <i>et al.</i>, “Unsupervised object learning via common fate,” in <i>2nd Conference on Causal Learning and Reasoning</i>, Tübingen, Germany, 2023.","ama":"Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. In: <i>2nd Conference on Causal Learning and Reasoning</i>. ; 2023."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"FrLo"}],"conference":{"end_date":"2023-04-14","location":"Tübingen, Germany","start_date":"2023-04-11","name":"CLeaR: Conference on Causal Learning and Reasoning"},"article_processing_charge":"No","type":"conference","day":"15"},{"publication_identifier":{"issn":["0031-9007"],"eissn":["1079-7114"]},"ec_funded":1,"arxiv":1,"isi":1,"external_id":{"arxiv":["2308.15247"],"isi":["001101784100001"],"pmid":["37595218"]},"date_published":"2023-08-04T00:00:00Z","date_updated":"2025-04-14T07:48:54Z","project":[{"grant_number":"801770","name":"Angulon: physics and applications of a new quasiparticle","_id":"2688CF98-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"publication_status":"published","title":"Nonadiabatic laser-induced alignment dynamics of molecules on a surface","quality_controlled":"1","abstract":[{"text":"We demonstrate that a sodium dimer, Na2(13Σ+u), residing on the surface of a helium nanodroplet, can be set into rotation by a nonresonant 1.0 ps infrared laser pulse. The time-dependent degree of alignment measured, exhibits a periodic, gradually decreasing structure that deviates qualitatively from that expected for gas-phase dimers. Comparison to alignment dynamics calculated from the time-dependent rotational Schrödinger equation shows that the deviation is due to the alignment dependent interaction between the dimer and the droplet surface. This interaction confines the dimer to the tangential plane of the droplet surface at the point where it resides and is the reason that the observed alignment dynamics is also well described by a 2D quantum rotor model.","lang":"eng"}],"type":"journal_article","acknowledgement":"H. S. acknowledges support from The Villum Foundation through a Villum Investigator Grant No. 25886. M. L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). F. J. and R. E. Z. acknowledge support from the Centre for Scientific Computing, Aarhus and the JKU scientific computing administration, Linz, respectively.","pmid":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"Kranabetter, L., Kristensen, H. H., Ghazaryan, A., Schouder, C. A., Chatterley, A. S., Janssen, P., … Stapelfeldt, H. (2023). Nonadiabatic laser-induced alignment dynamics of molecules on a surface. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">https://doi.org/10.1103/PhysRevLett.131.053201</a>","chicago":"Kranabetter, Lorenz, Henrik H. Kristensen, Areg Ghazaryan, Constant A. Schouder, Adam S. Chatterley, Paul Janssen, Frank Jensen, Robert E. Zillich, Mikhail Lemeshko, and Henrik Stapelfeldt. “Nonadiabatic Laser-Induced Alignment Dynamics of Molecules on a Surface.” <i>Physical Review Letters</i>. American Physical Society, 2023. <a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">https://doi.org/10.1103/PhysRevLett.131.053201</a>.","short":"L. Kranabetter, H.H. Kristensen, A. Ghazaryan, C.A. Schouder, A.S. Chatterley, P. Janssen, F. Jensen, R.E. Zillich, M. Lemeshko, H. Stapelfeldt, Physical Review Letters 131 (2023).","ieee":"L. Kranabetter <i>et al.</i>, “Nonadiabatic laser-induced alignment dynamics of molecules on a surface,” <i>Physical Review Letters</i>, vol. 131, no. 5. American Physical Society, 2023.","ama":"Kranabetter L, Kristensen HH, Ghazaryan A, et al. Nonadiabatic laser-induced alignment dynamics of molecules on a surface. <i>Physical Review Letters</i>. 2023;131(5). doi:<a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">10.1103/PhysRevLett.131.053201</a>","mla":"Kranabetter, Lorenz, et al. “Nonadiabatic Laser-Induced Alignment Dynamics of Molecules on a Surface.” <i>Physical Review Letters</i>, vol. 131, no. 5, 053201, American Physical Society, 2023, doi:<a href=\"https://doi.org/10.1103/PhysRevLett.131.053201\">10.1103/PhysRevLett.131.053201</a>.","ista":"Kranabetter L, Kristensen HH, Ghazaryan A, Schouder CA, Chatterley AS, Janssen P, Jensen F, Zillich RE, Lemeshko M, Stapelfeldt H. 2023. Nonadiabatic laser-induced alignment dynamics of molecules on a surface. Physical Review Letters. 131(5), 053201."},"publication":"Physical Review Letters","month":"08","oa":1,"article_number":"053201","oa_version":"Preprint","status":"public","_id":"14238","intvolume":"       131","scopus_import":"1","doi":"10.1103/PhysRevLett.131.053201","date_created":"2023-08-27T22:01:16Z","volume":131,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2308.15247"}],"author":[{"first_name":"Lorenz","full_name":"Kranabetter, Lorenz","last_name":"Kranabetter"},{"first_name":"Henrik H.","full_name":"Kristensen, Henrik H.","last_name":"Kristensen"},{"orcid":"0000-0001-9666-3543","last_name":"Ghazaryan","full_name":"Ghazaryan, Areg","first_name":"Areg","id":"4AF46FD6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Schouder, Constant A.","last_name":"Schouder","first_name":"Constant A."},{"full_name":"Chatterley, Adam S.","last_name":"Chatterley","first_name":"Adam S."},{"full_name":"Janssen, Paul","last_name":"Janssen","first_name":"Paul"},{"last_name":"Jensen","full_name":"Jensen, Frank","first_name":"Frank"},{"full_name":"Zillich, Robert E.","last_name":"Zillich","first_name":"Robert E."},{"first_name":"Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail","last_name":"Lemeshko"},{"last_name":"Stapelfeldt","full_name":"Stapelfeldt, Henrik","first_name":"Henrik"}],"issue":"5","day":"04","article_type":"original","publisher":"American Physical Society","department":[{"_id":"MiLe"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"year":"2023"},{"day":"03","publisher":"Cambridge University Press","article_type":"original","department":[{"_id":"TaHa"}],"has_accepted_license":"1","article_processing_charge":"Yes","language":[{"iso":"eng"}],"year":"2023","month":"08","oa":1,"article_number":"e66","status":"public","oa_version":"Published Version","file_date_updated":"2023-09-05T06:43:11Z","_id":"14239","ddc":["510"],"intvolume":"        11","scopus_import":"1","doi":"10.1017/fms.2023.65","date_created":"2023-08-27T22:01:16Z","volume":11,"author":[{"first_name":"Mirko","id":"2cf70c34-09c1-11ed-bd8d-c34fac206130","last_name":"Mauri","full_name":"Mauri, Mirko"},{"full_name":"Shinder, Evgeny","last_name":"Shinder","first_name":"Evgeny"}],"file":[{"file_size":280865,"file_id":"14266","relation":"main_file","content_type":"application/pdf","checksum":"c36241750cc5cb06890aec0ecdfee626","file_name":"2023_ForumMathematics_Mauri.pdf","date_created":"2023-09-05T06:43:11Z","success":1,"date_updated":"2023-09-05T06:43:11Z","creator":"dernst","access_level":"open_access"}],"type":"journal_article","acknowledgement":"We thank Agnieszka Bodzenta-Skibińska, Paolo Cascini, Wahei Hara, Sándor Kovács, Alexander Kuznetsov, Mircea Musta  ă, Nebojsa Pavic, Pavel Sechin, and Michael Wemyss for discussions and e-mail correspondence. We also thank the anonymous referee for the helpful comments. M.M. was supported by the Institute of Science and Technology Austria. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101034413. E.S. was partially supported by the EPSRC grant EP/T019379/1 “Derived categories and algebraic K-theory of singularities”, and by the ERC Synergy grant “Modern Aspects of Geometry: Categories, Cycles and Cohomology of Hyperkähler Varieties.”\r\n\r\n","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Mauri M, Shinder E. Homological Bondal-Orlov localization conjecture for rational singularities. <i>Forum of Mathematics, Sigma</i>. 2023;11. doi:<a href=\"https://doi.org/10.1017/fms.2023.65\">10.1017/fms.2023.65</a>","ieee":"M. Mauri and E. Shinder, “Homological Bondal-Orlov localization conjecture for rational singularities,” <i>Forum of Mathematics, Sigma</i>, vol. 11. Cambridge University Press, 2023.","ista":"Mauri M, Shinder E. 2023. Homological Bondal-Orlov localization conjecture for rational singularities. Forum of Mathematics, Sigma. 11, e66.","mla":"Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization Conjecture for Rational Singularities.” <i>Forum of Mathematics, Sigma</i>, vol. 11, e66, Cambridge University Press, 2023, doi:<a href=\"https://doi.org/10.1017/fms.2023.65\">10.1017/fms.2023.65</a>.","chicago":"Mauri, Mirko, and Evgeny Shinder. “Homological Bondal-Orlov Localization Conjecture for Rational Singularities.” <i>Forum of Mathematics, Sigma</i>. Cambridge University Press, 2023. <a href=\"https://doi.org/10.1017/fms.2023.65\">https://doi.org/10.1017/fms.2023.65</a>.","apa":"Mauri, M., &#38; Shinder, E. (2023). Homological Bondal-Orlov localization conjecture for rational singularities. <i>Forum of Mathematics, Sigma</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/fms.2023.65\">https://doi.org/10.1017/fms.2023.65</a>","short":"M. Mauri, E. Shinder, Forum of Mathematics, Sigma 11 (2023)."},"publication":"Forum of Mathematics, Sigma","publication_identifier":{"eissn":["2050-5094"]},"corr_author":"1","ec_funded":1,"arxiv":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"arxiv":["2212.06786"],"isi":["001041926700001"]},"isi":1,"date_published":"2023-08-03T00:00:00Z","date_updated":"2025-04-14T07:54:52Z","project":[{"call_identifier":"H2020","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","grant_number":"101034413"}],"title":"Homological Bondal-Orlov localization conjecture for rational singularities","publication_status":"published","abstract":[{"lang":"eng","text":"Given a resolution of rational singularities  π:X~→X  over a field of characteristic zero, we use a Hodge-theoretic argument to prove that the image of the functor  Rπ∗:Db(X~)→Db(X)\r\n  between bounded derived categories of coherent sheaves generates  Db(X)\r\n  as a triangulated category. This gives a weak version of the Bondal–Orlov localization conjecture [BO02], answering a question from [PS21]. The same result is established more generally for proper (not necessarily birational) morphisms  π:X~→X , with  X~\r\n  smooth, satisfying  Rπ∗(OX~)=OX ."}],"quality_controlled":"1"},{"scopus_import":"1","doi":"10.1145/3592098","intvolume":"        42","author":[{"first_name":"Stefan","id":"44D6411A-F248-11E8-B48F-1D18A9856A87","full_name":"Jeschke, Stefan","last_name":"Jeschke"},{"first_name":"Christopher J","id":"3C61F1D2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6646-5546","last_name":"Wojtan","full_name":"Wojtan, Christopher J"}],"date_created":"2023-08-27T22:01:17Z","volume":42,"oa":1,"month":"08","status":"public","oa_version":"Published Version","_id":"14240","file_date_updated":"2024-01-02T09:34:27Z","ddc":["000"],"article_number":"83","language":[{"iso":"eng"}],"year":"2023","publisher":"Association for Computing Machinery","article_type":"original","issue":"4","day":"01","acknowledged_ssus":[{"_id":"ScienComp"}],"has_accepted_license":"1","article_processing_charge":"Yes (in subscription journal)","department":[{"_id":"ChWo"}],"date_updated":"2025-04-14T08:01:13Z","title":"Generalizing shallow water simulations with dispersive surface waves","publication_status":"published","project":[{"_id":"34bc2376-11ca-11ed-8bc3-9a3b3961a088","name":"Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena","grant_number":"101045083"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"isi":["001044671300049"]},"isi":1,"date_published":"2023-08-01T00:00:00Z","abstract":[{"text":"This paper introduces a novel method for simulating large bodies of water as a height field. At the start of each time step, we partition the waves into a bulk flow (which approximately satisfies the assumptions of the shallow water equations) and surface waves (which approximately satisfy the assumptions of Airy wave theory). We then solve the two wave regimes separately using appropriate state-of-the-art techniques, and re-combine the resulting wave velocities at the end of each step. This strategy leads to the first heightfield wave model capable of simulating complex interactions between both deep and shallow water effects, like the waves from a boat wake sloshing up onto a beach, or a dam break producing wave interference patterns and eddies. We also analyze the numerical dispersion created by our method and derive an exact correction factor for waves at a constant water depth, giving us a numerically perfect re-creation of theoretical water wave dispersion patterns.","lang":"eng"}],"quality_controlled":"1","publication_identifier":{"issn":["0730-0301"],"eissn":["1557-7368"]},"corr_author":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"S. Jeschke, C. Wojtan, ACM Transactions on Graphics 42 (2023).","apa":"Jeschke, S., &#38; Wojtan, C. (2023). Generalizing shallow water simulations with dispersive surface waves. <i>ACM Transactions on Graphics</i>. Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3592098\">https://doi.org/10.1145/3592098</a>","chicago":"Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations with Dispersive Surface Waves.” <i>ACM Transactions on Graphics</i>. Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3592098\">https://doi.org/10.1145/3592098</a>.","ista":"Jeschke S, Wojtan C. 2023. Generalizing shallow water simulations with dispersive surface waves. ACM Transactions on Graphics. 42(4), 83.","mla":"Jeschke, Stefan, and Chris Wojtan. “Generalizing Shallow Water Simulations with Dispersive Surface Waves.” <i>ACM Transactions on Graphics</i>, vol. 42, no. 4, 83, Association for Computing Machinery, 2023, doi:<a href=\"https://doi.org/10.1145/3592098\">10.1145/3592098</a>.","ama":"Jeschke S, Wojtan C. Generalizing shallow water simulations with dispersive surface waves. <i>ACM Transactions on Graphics</i>. 2023;42(4). doi:<a href=\"https://doi.org/10.1145/3592098\">10.1145/3592098</a>","ieee":"S. Jeschke and C. Wojtan, “Generalizing shallow water simulations with dispersive surface waves,” <i>ACM Transactions on Graphics</i>, vol. 42, no. 4. Association for Computing Machinery, 2023."},"publication":"ACM Transactions on Graphics","file":[{"content_type":"video/mp4","relation":"main_file","checksum":"1d178bb2f8011d9f5aedda6427e18c7a","file_id":"14704","file_size":511572575,"date_updated":"2023-12-21T12:26:40Z","success":1,"creator":"sjeschke","access_level":"open_access","date_created":"2023-12-21T12:26:40Z","file_name":"PaperVideo_final.mp4"},{"creator":"dernst","access_level":"open_access","date_updated":"2024-01-02T09:34:27Z","success":1,"file_name":"2023_ACMToG_Jeschke.pdf","date_created":"2024-01-02T09:34:27Z","checksum":"a49b2e744d5cd1276bb8b2e0ce6dc638","relation":"main_file","content_type":"application/pdf","file_size":7469177,"file_id":"14725"}],"acknowledgement":"We thank Georg Sperl for helping with early research for this paper, Mickael Ly and Yi-Lu Chen for proofreading, and members of the ISTA Visual Computing Group for general feedback. This project was funded in part by the European Research Council (ERC Consolidator Grant 101045083 CoDiNA).\r\nThe motorboat and sailboat were modeled by Sergei and the palmtrees by YadroGames. The environment map was created by Emil Persson.","type":"journal_article"},{"article_number":"20","status":"public","oa_version":"Preprint","_id":"14241","month":"07","oa":1,"date_created":"2023-08-27T22:01:17Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2305.05944","open_access":"1"}],"author":[{"full_name":"Tojo, Kenji","last_name":"Tojo","first_name":"Kenji"},{"full_name":"Shamir, Ariel","last_name":"Shamir","first_name":"Ariel"},{"orcid":"0000-0001-6511-9385","last_name":"Bickel","full_name":"Bickel, Bernd","first_name":"Bernd","id":"49876194-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Nobuyuki","full_name":"Umetani, Nobuyuki","last_name":"Umetani"}],"scopus_import":"1","doi":"10.1145/3588432.3591542","department":[{"_id":"BeBi"}],"article_processing_charge":"No","day":"23","publisher":"Association for Computing Machinery","language":[{"iso":"eng"}],"year":"2023","arxiv":1,"publication_identifier":{"isbn":["9798400701597"]},"corr_author":"1","abstract":[{"lang":"eng","text":"We present a technique to optimize the reflectivity of a surface while preserving its overall shape. The naïve optimization of the mesh vertices using the gradients of reflectivity simulations results in undesirable distortion. In contrast, our robust formulation optimizes the surface normal as an independent variable that bridges the reflectivity term with differential rendering, and the regularization term with as-rigid-as-possible elastic energy. We further adaptively subdivide the input mesh to improve the convergence. Consequently, our method can minimize the retroreflectivity of a wide range of input shapes, resulting in sharply creased shapes ubiquitous among stealth aircraft and Sci-Fi vehicles. Furthermore, by changing the reward for the direction of the outgoing light directions, our method can be applied to other reflectivity design tasks, such as the optimization of architectural walls to concentrate light in a specific region. We have tested the proposed method using light-transport simulations and real-world 3D-printed objects."}],"quality_controlled":"1","isi":1,"external_id":{"isi":["001117690500020"],"arxiv":["2305.05944"]},"date_published":"2023-07-23T00:00:00Z","date_updated":"2025-09-09T12:49:15Z","title":"Stealth shaper: Reflectivity optimization as surface stylization","publication_status":"published","conference":{"end_date":"2023-08-10","location":"Los Angeles, CA, United States","start_date":"2023-08-06","name":"SIGGRAPH: Computer Graphics and Interactive Techniques Conference"},"type":"conference","acknowledgement":"The authors would like to thank Yuki Koyama and Takeo Igarashi for early discussions, and Yuta Yaguchi for support in 3D printing. This research is partially supported by the Israel Science Foundation grant number 1390/19.\r\n","publication":"SIGGRAPH 2023 Conference Proceedings","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","citation":{"mla":"Tojo, Kenji, et al. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.” <i>SIGGRAPH 2023 Conference Proceedings</i>, 20, Association for Computing Machinery, 2023, doi:<a href=\"https://doi.org/10.1145/3588432.3591542\">10.1145/3588432.3591542</a>.","ista":"Tojo K, Shamir A, Bickel B, Umetani N. 2023. Stealth shaper: Reflectivity optimization as surface stylization. SIGGRAPH 2023 Conference Proceedings. SIGGRAPH: Computer Graphics and Interactive Techniques Conference, 20.","ama":"Tojo K, Shamir A, Bickel B, Umetani N. Stealth shaper: Reflectivity optimization as surface stylization. In: <i>SIGGRAPH 2023 Conference Proceedings</i>. Association for Computing Machinery; 2023. doi:<a href=\"https://doi.org/10.1145/3588432.3591542\">10.1145/3588432.3591542</a>","ieee":"K. Tojo, A. Shamir, B. Bickel, and N. Umetani, “Stealth shaper: Reflectivity optimization as surface stylization,” in <i>SIGGRAPH 2023 Conference Proceedings</i>, Los Angeles, CA, United States, 2023.","short":"K. Tojo, A. Shamir, B. Bickel, N. Umetani, in:, SIGGRAPH 2023 Conference Proceedings, Association for Computing Machinery, 2023.","apa":"Tojo, K., Shamir, A., Bickel, B., &#38; Umetani, N. (2023). Stealth shaper: Reflectivity optimization as surface stylization. In <i>SIGGRAPH 2023 Conference Proceedings</i>. Los Angeles, CA, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3588432.3591542\">https://doi.org/10.1145/3588432.3591542</a>","chicago":"Tojo, Kenji, Ariel Shamir, Bernd Bickel, and Nobuyuki Umetani. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.” In <i>SIGGRAPH 2023 Conference Proceedings</i>. Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3588432.3591542\">https://doi.org/10.1145/3588432.3591542</a>."}}]