| MLP-Mixer: An All-MLP Architecture for Vision I Tolstikhin, N Houlsby, A Kolesnikov, L Beyer, X Zhai, T Unterthiner, ... Neural Information Processing Systems, 2021 | 2868 | 2021 |
| ViViT: A Video Vision Transformer A Arnab, M Dehghani, G Heigold, C Sun, M Lučić*, C Schmid* International Conference on Computer Vision, 2021 | 2496 | 2021 |
| Gemini: A family of highly capable multimodal models Gemini Team arXiv preprint arXiv:2312.11805, 2023 | 2239 | 2023 |
| Challenging common assumptions in the unsupervised learning of disentangled representations F Locatello, S Bauer, M Lucic, S Gelly, B Schölkopf, O Bachem International Conference on Machine Learning (Best Paper Award), 2019 | 1637 | 2019 |
| Are GANs Created Equal? A Large-scale Study M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet Advances in neural information processing systems 31, 2018 | 1270 | 2018 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research, 2020 | 796 | 2020 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context Gemini Team arXiv preprint arXiv:2403.05530, 2024 | 705 | 2024 |
| Assessing Generative Models via Precision and Recall MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly Advances in Neural Information Processing Systems, 2018 | 637 | 2018 |
| Recent advances in autoencoder-based representation learning M Tschannen, O Bachem, M Lucic Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018 | 592 | 2018 |
| On Mutual Information Maximization for Representation Learning M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic International Conference on Learning Representations, 2020 | 581 | 2020 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 485 | 2023 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... | 434* | 2019 |
| Self-Supervised GANs via Auxiliary Rotation Loss T Chen, X Zhai, M Ritter, M Lucic, N Houlsby Conference on Computer Vision and Pattern Recognition, 2019 | 398 | 2019 |
| A Large-Scale Study on Regularization and Normalization in GANs K Kurach, M Lucic, X Zhai, M Michalski, S Gelly International Conference on Machine Learning, 2018 | 378* | 2018 |
| Revisiting the Calibration of Modern Neural Networks M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ... Neural Information Processing Systems, 2021 | 347 | 2021 |
| Fast and provably good seedings for k-means O Bachem, M Lucic, H Hassani, A Krause Advances in Neural Information Processing Systems, 2016 | 195 | 2016 |
| Practical coreset constructions for machine learning O Bachem*, M Lucic*, A Krause arXiv preprint arXiv:1703.06476, 2017 | 189 | 2017 |
| High-Fidelity Image Generation With Fewer Labels M Lučić*, M Tschannen*, M Ritter*, X Zhai, O Bachem, S Gelly International Conference on Machine Learning, 2019 | 179 | 2019 |
| Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations MSM Sajjadi, H Meyer, E Pot, U Bergmann, K Greff, N Radwan, S Vora, ... Computer Vision and Pattern Recognition, 2022 | 178 | 2022 |
| Approximate K-Means++ in Sublinear Time O Bachem, M Lucic, SH Hassani, A Krause AAAI Conference on Artificial Intelligence, 2016 | 176 | 2016 |
Olivier BachemResearch Scientist, Google BrainEmail verificata su google.com
