| 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 | 1621 | 2021 |
| ViViT: A Video Vision Transformer A Arnab, M Dehghani, G Heigold, C Sun, M Lučić*, C Schmid* International Conference on Computer Vision, 2021 | 1369 | 2021 |
| 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 | 1297 | 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 | 1083 | 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 | 580 | 2020 |
| On Mutual Information Maximization for Representation Learning M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic International Conference on Learning Representations, 2020 | 474 | 2020 |
| Assessing Generative Models via Precision and Recall MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly Advances in Neural Information Processing Systems, 2018 | 454 | 2018 |
| Recent advances in autoencoder-based representation learning M Tschannen, O Bachem, M Lucic Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018 | 448 | 2018 |
| 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 | 342 | 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 | 338* | 2018 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... | 263* | 2019 |
| Revisiting the Calibration of Modern Neural Networks M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ... Neural Information Processing Systems, 2021 | 188 | 2021 |
| Fast and provably good seedings for k-means O Bachem, M Lucic, H Hassani, A Krause Advances in Neural Information Processing Systems, 2016 | 182 | 2016 |
| Approximate K-Means++ in Sublinear Time O Bachem, M Lucic, SH Hassani, A Krause AAAI Conference on Artificial Intelligence, 2016 | 159 | 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, 2019 | 156 | 2019 |
| Practical coreset constructions for machine learning O Bachem*, M Lucic*, A Krause arXiv preprint arXiv:1703.06476, 2017 | 152 | 2017 |
| Scalable k-means clustering via lightweight coresets O Bachem, M Lucic, A Krause International Conference on Knowledge Discovery & Data Mining, 2018 | 147 | 2018 |
| 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 | 129 | 2023 |
| Training Gaussian mixture models at scale via coresets M Lucic, M Faulkner, A Krause, D Feldman The Journal of Machine Learning Research, 2017 | 121 | 2017 |
| Deep Generative Models for Distribution-Preserving Lossy Compression M Tschannen, E Agustsson, M Lucic Advances in Neural Information Processing Systems, 2018 | 120 | 2018 |
Olivier BachemResearch Scientist, Google BrainVerified email at google.com
