Francesco Locatello

Citat de

	Toate	Din 2020
Referințe bibliografice	7944	7696
h-index	33	31
i10-index	56	56

2300

1150

575

1725

2018201920202021202220232024202536 197 460 937 1579 2001 2267 440

Acces public

Afișați-le pe toate

24 de articole

0 articole

disponibile

indisponibile

Pe baza cerințelor privind finanțarea

Coautori

Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHAdresă de e-mail confirmată pe tuebingen.mpg.de
Stefan BauerHelmholtz | TUM | CIFARAdresă de e-mail confirmată pe tum.de
Gunnar RätschProfessor, ETH ZürichAdresă de e-mail confirmată pe inf.ethz.ch
Olivier BachemResearch Scientist, Google BrainAdresă de e-mail confirmată pe google.com
Frederik TräubleMax Planck Institute for Intelligent SystemsAdresă de e-mail confirmată pe tuebingen.mpg.de
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARAdresă de e-mail confirmată pe umontreal.ca
Sylvain GellyGoogle Brain ZurichAdresă de e-mail confirmată pe m4x.org
Peter GehlerTübingen AI Center, University TübingenAdresă de e-mail confirmată pe uni-tuebingen.de
Andrea DittadiHelmholtz AI Munich | TUMAdresă de e-mail confirmată pe tum.de
Julius von KügelgenETH ZürichAdresă de e-mail confirmată pe stat.math.ethz.ch
Chris RussellAssociate Professor, University of OxfordAdresă de e-mail confirmată pe oii.ox.ac.uk
Mario LučićResearch Scientist, Google DeepMindAdresă de e-mail confirmată pe google.com
Dominik ZietlowLead Analyst, Second FoundationAdresă de e-mail confirmată pe second-foundation.eu
Manuel WüthrichHarvard UniversityAdresă de e-mail confirmată pe seas.harvard.edu
Max HornApplied Scientist in Causal Representation Learning, Amazon AWS AIAdresă de e-mail confirmată pe amazon.de
Georg HeigoldResearch Scientist, Google Inc.Adresă de e-mail confirmată pe google.com
Thomas KipfStaff Research Scientist, Google DeepMindAdresă de e-mail confirmată pe google.com
Thomas UnterthinerGoogle DeepMindAdresă de e-mail confirmată pe pm.me
Dirk WeissenbornInceptive Inc.Adresă de e-mail confirmată pe inceptive.team
Aravindh MahendranGoogle DeepmindAdresă de e-mail confirmată pe google.com

Urmăriți

Francesco Locatello

Assistant Professor, ISTA

Adresă de e-mail confirmată pe ista.ac.at - Pagina de pornire

Causality Deep Learning Machine Learning


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
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	1690	2019
Towards Causal Representation Learning B Schölkopf, F Locatello, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ... Proceedings of the IEEE, 2021	1442	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	907	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	366	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	330	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	256	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	209	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	202	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	149	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	136	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	97	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	94	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	93	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	93	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	77	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	77	2020
Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022	75	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

Sistemul nu poate realiza operația în acest moment. Încercați din nou mai târziu.

Articole 1–20

Referințe bibliografice pe an

Citate duplicat

Citate fuzionate

Adăugați coautoriCoautori

Urmăriți

Citat de

Coautori