Francesco Locatello

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHCorreu electrònic verificat a tuebingen.mpg.de
Stefan BauerHelmholtz | TUM | CIFARCorreu electrònic verificat a tum.de
Gunnar RätschProfessor, ETH ZürichCorreu electrònic verificat a inf.ethz.ch
Olivier BachemResearch Scientist, Google BrainCorreu electrònic verificat a google.com
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARCorreu electrònic verificat a umontreal.ca
Frederik TräubleMax Planck Institute for Intelligent SystemsCorreu electrònic verificat a tuebingen.mpg.de
Sylvain GellyGoogle Brain ZurichCorreu electrònic verificat a m4x.org
Peter GehlerZalandoCorreu electrònic verificat a zalando.de
Andrea DittadiHelmholtz AI Munich | TUMCorreu electrònic verificat a tum.de
Julius von KügelgenETH ZürichCorreu electrònic verificat a stat.math.ethz.ch
Chris RussellAssociate Professor, University of OxfordCorreu electrònic verificat a oii.ox.ac.uk
Mario LučićResearch Scientist, Google DeepMindCorreu electrònic verificat a google.com
Dominik ZietlowLead Analyst, Second FoundationCorreu electrònic verificat a second-foundation.eu
Manuel WüthrichHarvard UniversityCorreu electrònic verificat a seas.harvard.edu
Max HornApplied Scientist in Causal Representation Learning, Amazon AWS AICorreu electrònic verificat a amazon.de
Thomas BroxUniversity of FreiburgCorreu electrònic verificat a cs.uni-freiburg.de
Marco FumeroPostDoctoral Researcher, Institute of Science and Technology Austria (ISTA)Correu electrònic verificat a ist.ac.at
Michael TschannenGoogle DeepMindCorreu electrònic verificat a google.com
Emanuele RodolàProfessor of Computer Science, Sapienza University of RomeCorreu electrònic verificat a di.uniroma1.it
Georg HeigoldResearch Scientist, Google Inc.Correu electrònic verificat a google.com

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Francesco Locatello

Assistant Professor, ISTA

Correu electrònic verificat a ista.ac.at - Pàgina d'inici

Causality Deep Learning Machine Learning


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
Challenging common assumptions in the unsupervised learning of disentangled representations F Locatello, S Bauer, M Lucic, G Rätsch, S Gelly, B Schölkopf, O Bachem ICML 2019 - Proceedings of the 36th International Conference on Machine …, 2019	1678	2019
Towards Causal Representation Learning B Schölkopf, F Locatello, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ... Proceedings of the IEEE, 2021	1417	2021
Object-Centric Learning with Slot Attention F Locatello*, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ... NeurIPS 2020 - Thirty-fourth Conference on Neural Information Processing …, 2020	883	2020
Weakly-Supervised Disentanglement Without Compromises F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen ICML 2020 - Proceedings of the 37th International Conference on Machine Learning, 2020	360	2020
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J von Kügelgen, Y Sharma, L Gresele, W Brendel, B Schölkopf, ... NeurIPS 2021 - Thirty-fifth Conference on Neural Information Processing Systems, 2021	324	2021
On the Fairness of Disentangled Representations F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019	252	2019
Are Disentangled Representations Helpful for Abstract Visual Reasoning? S van Steenkiste, F Locatello, J Schmidhuber, O Bachem NeurIPS 2019: Thirty-third Conference on Neural Information Processing Systems, 2019	224	2019
Disentangling factors of variation using few labels F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem ICLR 2020 - 8th International Conference on Learning Representations, 2020	208	2020
SOM-VAE: Interpretable discrete representation learning on time series V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch ICLR 2019 - Seventh International Conference on Learning Representations, 2018	201	2018
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset MW Gondal, M Wüthrich, Đ Miladinović, F Locatello, M Breidt, V Volchkov, ... NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019	151	2019
On Disentangled Representations Learned From Correlated Data F Träuble, E Creager, N Kilbertus, F Locatello, A Dittadi, A Goyal, ... ICML 2021 - Proceedings of the 38th International Conference on Machine Learning, 2021	139	2021
Bridging the gap to real-world object-centric learning M Seitzer, M Horn, A Zadaianchuk, D Zietlow, T Xiao, CJ Simon-Gabriel, ... ICLR 2023, 2023	133	2023
The incomplete rosetta stone problem: Identifiability results for multi-view nonlinear ica L Gresele, PK Rubenstein, A Mehrjou, F Locatello, B Schölkopf UAI 2019 - Conference on Uncertainty in Artificial Intelligence, 2019	94	2019
On the transfer of disentangled representations in realistic settings A Dittadi, F Träuble, F Locatello, M Wüthrich, V Agrawal, O Winther, ... ICLR 2021 - 9th International Conference on Learning Representations, 2020	92	2020
Score matching enables causal discovery of nonlinear additive noise models P Rolland, V Cevher, M Kleindessner, C Russel, B Schölkopf, D Janzing, ... ICML 2022, 2022	89	2022
Relative representations enable zero-shot latent space communication L Moschella, V Maiorca, M Fumero, A Norelli, F Locatello, E Rodolà ICLR 2023 (Oral), 2023	88	2023
Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022	78	2022
Visual representation learning does not generalize strongly within the same domain L Schott, J von Kügelgen, F Träuble, P Gehler, C Russell, M Bethge, ... ICLR 2022, 2022	74	2022
Generalization and robustness implications in object-centric learning A Dittadi, S Papa, M De Vita, B Schölkopf, O Winther, F Locatello ICML 2022, 2022	73	2022
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation F Locatello, S Bauer, M Lucic, G Rätsch, S Gelly, B Schölkopf, O Bachem Journal of Machine Learning Research (JMLR), 2020	73	2020

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