Prati
Olivier Bachem
Olivier Bachem
Research Scientist, Google Brain
Potvrđena adresa e-pošte na google.com - Početna stranica
Naslov
Citirano
Citirano
Godina
Challenging common assumptions in the unsupervised learning of disentangled representations
F Locatello, S Bauer, M Lucic, G Raetsch, S Gelly, B Schölkopf, O Bachem
international conference on machine learning, 4114-4124, 2019
10132019
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
arXiv preprint arXiv:1812.05069, 2018
3312018
Assessing generative models via precision and recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
Advances in neural information processing systems 31, 2018
3122018
Weakly-supervised disentanglement without compromises
F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen
International Conference on Machine Learning, 6348-6359, 2020
1822020
On the fairness of disentangled representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
Advances in neural information processing systems 32, 2019
1592019
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in neural information processing systems 29, 2016
1552016
Google research football: A novel reinforcement learning environment
K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ...
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4501-4510, 2020
1542020
Are disentangled representations helpful for abstract visual reasoning?
S Van Steenkiste, F Locatello, J Schmidhuber, O Bachem
Advances in Neural Information Processing Systems 32, 2019
1392019
High-fidelity image generation with fewer labels
M Lučić, M Tschannen, M Ritter, X Zhai, O Bachem, S Gelly
International conference on machine learning, 4183-4192, 2019
1312019
K-mc2: approximate k-means++ in sublinear time
O Bachem, M Lucic, H Hassani, A Krause
AAAI 2016, 2016
130*2016
A large-scale study of representation learning with the visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
arXiv preprint arXiv:1910.04867, 2019
1252019
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
1232018
Practical coreset constructions for machine learning
O Bachem, M Lucic, A Krause
arXiv preprint arXiv:1703.06476, 2017
1222017
Disentangling factors of variation using few labels
F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem
arXiv preprint arXiv:1905.01258, 2019
1192019
What matters in on-policy reinforcement learning? a large-scale empirical study
M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ...
arXiv preprint arXiv:2006.05990, 2020
1042020
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
MW Gondal, M Wuthrich, D Miladinovic, F Locatello, M Breidt, V Volchkov, ...
Advances in Neural Information Processing Systems 32, 2019
862019
Strong coresets for hard and soft Bregman clustering with applications to exponential family mixtures
M Lucic, O Bachem, A Krause
Artificial intelligence and statistics, 1-9, 2016
862016
Coresets for nonparametric estimation-the case of DP-means
O Bachem, M Lucic, A Krause
International Conference on Machine Learning, 209-217, 2015
822015
Brax--A Differentiable Physics Engine for Large Scale Rigid Body Simulation
CD Freeman, E Frey, A Raichuk, S Girgin, I Mordatch, O Bachem
arXiv preprint arXiv:2106.13281, 2021
652021
What matters for on-policy deep actor-critic methods? a large-scale study
M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ...
International conference on learning representations, 2021
652021
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