Francesco Locatello
Francesco Locatello
PhD student, ETH Zürich, Max Planck Institute for Intelligent Systems
Verified email at ethz.ch
Title
Cited by
Cited by
Year
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
2852019
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
432018
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
382017
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
352019
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
302019
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
282020
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
192017
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
182019
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
182017
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
172018
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
15*2018
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
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
142020
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
142018
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
82019
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
52020
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
4*2019
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
G Négiar, G Dresdner, A Tsai, LE Ghaoui, F Locatello, RM Freund, ...
ICML 2020 - Proceedings of the 37th International Conference on Machine Learning, 2020
32020
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
22020
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
22020
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