| Dynamic Pricing in Ridesharing Platforms S Banerjee, R Johari, C Riquelme ACM SIGecom Exchanges 15 (1), 65-70, 2016 | 538* | 2016 |
| Pali: A jointly-scaled multilingual language-image model X Chen, X Wang, S Changpinyo, AJ Piergiovanni, P Padlewski, D Salz, ... arXiv preprint arXiv:2209.06794, 2022 | 472 | 2022 |
| Scaling vision with sparse mixture of experts C Riquelme, J Puigcerver, B Mustafa, M Neumann, R Jenatton, ... Advances in Neural Information Processing Systems 34, 8583-8595, 2021 | 408 | 2021 |
| Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling C Riquelme, G Tucker, J Snoek Sixth International Conference on Learning Representations, ICLR 2018., 2018 | 372 | 2018 |
| 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 | 360 | 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 | 331 | 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 | 291 | 2019 |
| Multimodal contrastive learning with limoe: the language-image mixture of experts B Mustafa, C Riquelme, J Puigcerver, R Jenatton, N Houlsby Advances in Neural Information Processing Systems 35, 9564-9576, 2022 | 115 | 2022 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... | 72 | 2019 |
| Human Interaction with Recommendation Systems S Schmit, C Riquelme The 21st International Conference on Artificial Intelligence and Statistics …, 2018 | 62 | 2018 |
| Scalable transfer learning with expert models J Puigcerver, C Riquelme, B Mustafa, C Renggli, AS Pinto, S Gelly, ... arXiv preprint arXiv:2009.13239, 2020 | 58 | 2020 |
| From sparse to soft mixtures of experts J Puigcerver, C Riquelme, B Mustafa, N Houlsby arXiv preprint arXiv:2308.00951, 2023 | 52 | 2023 |
| Deep bayesian bandits showdown C Riquelme, G Tucker, J Snoek International conference on learning representations 9, 2018 | 51 | 2018 |
| 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 | 47 | 2019 |
| Learning to merge tokens in vision transformers C Renggli, AS Pinto, N Houlsby, B Mustafa, J Puigcerver, C Riquelme arXiv preprint arXiv:2202.12015, 2022 | 42 | 2022 |
| On last-layer algorithms for classification: Decoupling representation from uncertainty estimation N Brosse, C Riquelme, A Martin, S Gelly, É Moulines arXiv preprint arXiv:2001.08049, 2020 | 34 | 2020 |
| Online Active Linear Regression via Thresholding C Riquelme, R Johari, B Zhang AAAI-17, Thirty-First AAAI Conference on Artificial Intelligence., 2017 | 33 | 2017 |
| The Beta-VAE's Implicit Prior MD Hoffman, C Riquelme, M Johnson Bayesian Deep Learning Workshop. Neural Information Processing Systems, NIPS …, 2017 | 20 | 2017 |
| Active Learning for Accurate Estimation of Linear Models C Riquelme, M Ghavamzadeh, A Lazaric Thirty-fourth International Conference on Machine Learning, ICML 2017., 2017 | 18 | 2017 |
| Stable lm 2 1.6 b technical report M Bellagente, J Tow, D Mahan, D Phung, M Zhuravinskyi, R Adithyan, ... arXiv preprint arXiv:2402.17834, 2024 | 16 | 2024 |
Ramesh JohariProfessor, Management Science and Engineering, Stanford University在 stanford.edu 的电子邮件经过验证
