| On mutual information maximization for representation learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 495 | 2019 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 407 | 2023 |
| 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 | 262 | 2019 |
| 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 | 251 | 2023 |
| Revisiting the calibration of modern neural networks M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ... Advances in Neural Information Processing Systems 34, 15682-15694, 2021 | 251 | 2021 |
| High-dimensional gaussian process bandits J Djolonga, A Krause, V Cevher Advances in neural information processing systems 26, 2013 | 198 | 2013 |
| Fast differentiable sorting and ranking M Blondel, O Teboul, Q Berthet, J Djolonga International Conference on Machine Learning, 950-959, 2020 | 188 | 2020 |
| On robustness and transferability of convolutional neural networks J Djolonga, J Yung, M Tschannen, R Romijnders, L Beyer, A Kolesnikov, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 127 | 2021 |
| Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 97 | 2021 |
| Differentiable learning of submodular models J Djolonga, A Krause Advances in Neural Information Processing Systems 30, 2017 | 95 | 2017 |
| You only train once: Loss-conditional training of deep networks A Dosovitskiy, J Djolonga International conference on learning representations, 2019 | 84 | 2019 |
| From map to marginals: Variational inference in bayesian submodular models J Djolonga, A Krause Advances in Neural Information Processing Systems 27, 2014 | 84 | 2014 |
| Pali-x: On scaling up a multilingual vision and language model X Chen, J Djolonga, P Padlewski, B Mustafa, S Changpinyo, J Wu, ... arXiv preprint arXiv:2305.18565, 2023 | 80 | 2023 |
| Self-supervised learning of video-induced visual invariances M Tschannen, J Djolonga, M Ritter, A Mahendran, N Houlsby, S Gelly, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 69 | 2020 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... | 64 | 2019 |
| Practical and consistent estimation of f-divergences P Rubenstein, O Bousquet, J Djolonga, C Riquelme, IO Tolstikhin Advances in Neural Information Processing Systems 32, 2019 | 44 | 2019 |
| Learning probabilistic submodular diversity models via noise contrastive estimation S Tschiatschek, J Djolonga, A Krause Artificial Intelligence and Statistics, 770-779, 2016 | 33 | 2016 |
| Scalable variational inference in log-supermodular models J Djolonga, A Krause International Conference on Machine Learning, 1804-1813, 2015 | 28 | 2015 |
| Variational inference in mixed probabilistic submodular models J Djolonga, S Tschiatschek, A Krause Advances in Neural Information Processing Systems 29, 2016 | 23 | 2016 |
| Precision-recall curves using information divergence frontiers J Djolonga, M Lucic, M Cuturi, O Bachem, O Bousquet, S Gelly International Conference on Artificial Intelligence and Statistics, 2550-2559, 2020 | 21 | 2020 |
Andreas KrauseProfessor of Computer Science, ETH ZurichAdresse e-mail validée de inf.ethz.ch
