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Olivier Bachem
Olivier Bachem
Research Scientist, Google Brain
Geverifieerd e-mailadres voor google.com - Homepage
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Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
25172023
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
16782019
Gemma: Open models based on gemini research and technology
G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ...
arXiv preprint arXiv:2403.08295, 2024
9292024
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
6562018
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
arXiv preprint arXiv:1812.05069, 2018
6192018
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
4392020
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
3742019
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
3612020
Gemma 2: Improving open language models at a practical size
G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ...
arXiv preprint arXiv:2408.00118, 2024
3352024
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
2832021
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
2602020
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
2522019
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
2252019
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
2082019
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
2072021
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in neural information processing systems 29, 2016
2052016
Practical coreset constructions for machine learning
O Bachem, M Lucic, A Krause
arXiv preprint arXiv:1703.06476, 2017
1912017
K-mc2: approximate k-means++ in sublinear time
O Bachem, M Lucic, H Hassani, A Krause
AAAI 2016, 2016
184*2016
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
1802019
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
1712018
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Artikelen 1–20