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Francesco Locatello
Francesco Locatello
Assistant Professor, ISTA
Preverjeni e-poštni naslov na ista.ac.at - Domača stran
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Navedeno
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Leto
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
14482019
Towards Causal Representation Learning
B Schölkopf*, F Locatello*, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ...
Proceedings of the IEEE, 2021
10262021
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
6362020
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
2892020
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
2392021
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
2162019
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
1952019
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
1822020
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
1792018
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
1272019
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
1122021
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
772020
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
752019
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
682023
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
F Locatello, R Khanna, M Tschannen, M Jaggi
AISTATS 2017 - Proceedings of the 20th International Conference on Artifcial …, 2017
622017
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
592020
Assaying out-of-distribution generalization in transfer learning
F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ...
NeurIPS 2022, 2022
532022
Generalization and robustness implications in object-centric learning
A Dittadi, S Papa, M De Vita, B Schölkopf, O Winther, F Locatello
ICML 2022, 2022
522022
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
522022
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
502022
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