Francesco Locatello

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHPatvirtintas el. paštas tuebingen.mpg.de
Stefan BauerHelmholtz | TUM | CIFARPatvirtintas el. paštas tum.de
Gunnar RätschProfessor, ETH ZürichPatvirtintas el. paštas inf.ethz.ch
Olivier BachemResearch Scientist, Google BrainPatvirtintas el. paštas google.com
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARPatvirtintas el. paštas umontreal.ca
Frederik TräubleMax Planck Institute for Intelligent SystemsPatvirtintas el. paštas tuebingen.mpg.de
Sylvain GellyGoogle Brain ZurichPatvirtintas el. paštas m4x.org
Peter GehlerZalandoPatvirtintas el. paštas zalando.de
Andrea DittadiHelmholtz AI Munich | TUMPatvirtintas el. paštas tum.de
Julius von KügelgenETH ZürichPatvirtintas el. paštas stat.math.ethz.ch
Chris RussellAssociate Professor, University of OxfordPatvirtintas el. paštas oii.ox.ac.uk
Mario LučićResearch Scientist, Google DeepMindPatvirtintas el. paštas google.com
Dominik ZietlowLead Analyst, Second FoundationPatvirtintas el. paštas second-foundation.eu
Manuel WüthrichHarvard UniversityPatvirtintas el. paštas seas.harvard.edu
Max HornApplied Scientist in Causal Representation Learning, Amazon AWS AIPatvirtintas el. paštas amazon.de
Thomas BroxUniversity of FreiburgPatvirtintas el. paštas cs.uni-freiburg.de
Marco FumeroPostDoctoral Researcher, Institute of Science and Technology Austria (ISTA)Patvirtintas el. paštas ist.ac.at
Michael TschannenGoogle DeepMindPatvirtintas el. paštas google.com
Emanuele RodolàProfessor of Computer Science, Sapienza University of RomePatvirtintas el. paštas di.uniroma1.it
Georg HeigoldResearch Scientist, Google Inc.Patvirtintas el. paštas google.com

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Francesco Locatello

Assistant Professor, ISTA

Patvirtintas el. paštas ista.ac.at - Pagrindinis puslapis

Causality Deep Learning Machine Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	1676	2019
Towards Causal Representation Learning B Schölkopf, F Locatello, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ... Proceedings of the IEEE, 2021	1409	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	879	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	361	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	322	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	225	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	200	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	140	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	131	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	93	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
Relative representations enable zero-shot latent space communication L Moschella, V Maiorca, M Fumero, A Norelli, F Locatello, E Rodolà ICLR 2023 (Oral), 2023	89	2023
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
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	75	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
Generalization and robustness implications in object-centric learning A Dittadi, S Papa, M De Vita, B Schölkopf, O Winther, F Locatello ICML 2022, 2022	72	2022

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