Francesco Locatello
Francesco Locatello
PhD student, ETH Zürich, Max Planck Institute for Intelligent Systems
Verifierad e-postadress på ethz.ch
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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 …, 2018
2302018
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
372017
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
332018
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
262019
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
222019
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, 2019
212019
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
F Locatello, M Tschannen, G Rätsch, M Jaggi
NIPS 2017 - Advances in Neural Information Processing Systems, 2017
172017
Boosting Black Box Variational Inference
F Locatello, G Dresdner, R Khanna, I Valera, G Rätsch
NeurIPS 2018 - Advances in Neural Information Processing Systems (Spotlight), 2018
152018
On Matching Pursuit and Coordinate Descent
F Locatello, A Raj, SP Reddy, G Rätsch, B Schölkopf, SU Stich, M Jaggi
ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, 2018
15*2018
Boosting Variational Inference: an Optimization Perspective
F Locatello, R Khanna, J Ghosh, G Rätsch
AISTATS 2018 - Proceedings of the 21th International Conference on Artifcial …, 2017
152017
Competitive Training of Mixtures of Independent Deep Generative Models
F Locatello, D Vincent, I Tolstikhin, G Rätsch, S Gelly, B Schölkopf
arXiv preprint arXiv:1804.11130, 2018
14*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
132019
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
A Yurtsever, O Fercoq, F Locatello, V Cevher
ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, 2018
132018
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
52020
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
42019
Stochastic Frank-Wolfe for Composite Convex Minimization
F Locatello, A Yurtsever, O Fercoq, V Cevher
NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019
3*2019
Object-Centric Learning with Slot Attention
F Locatello*, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ...
arXiv preprint arXiv:2006.15055, 2020
22020
A Commentary on the Unsupervised Learning of Disentangled Representations
F Locatello, S Bauer, M Lucic, G Rätsch, S Gelly, B Schölkopf, O Bachem
AAAI 2020, 2020
12020
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data
F Träuble, E Creager, N Kilbertus, A Goyal, F Locatello, B Schölkopf, ...
arXiv preprint arXiv:2006.07886, 2020
12020
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
G Négiar, G Dresdner, A Tsai, LE Ghaoui, F Locatello, F Pedregosa
ICML 2020 - Proceedings of the 37th International Conference on Machine Learning, 2020
12020
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Artiklar 1–20