| Data-efficient image recognition with contrastive predictive coding OJ Hénaff, A Srinivas, J De Fauw, A Razavi, C Doersch, SMA Eslami, ... International Conference on Machine Learning, 4182-4192, 2020 | 1346 | 2020 |
| Perceiver io: A general architecture for structured inputs & outputs A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ... arXiv preprint arXiv:2107.14795, 2021 | 356 | 2021 |
| Are we done with imagenet? L Beyer, OJ Hénaff, A Kolesnikov, X Zhai, A Oord arXiv preprint arXiv:2006.07159, 2020 | 297 | 2020 |
| Efficient visual pretraining with contrastive detection OJ Hénaff, S Koppula, JB Alayrac, A Van den Oord, O Vinyals, J Carreira Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 124 | 2021 |
| Perceptual straightening of natural videos OJ Hénaff, RLT Goris, EP Simoncelli Nature neuroscience 22 (6), 984-991, 2019 | 77 | 2019 |
| Divide and contrast: Self-supervised learning from uncurated data Y Tian, OJ Henaff, A van den Oord Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 63 | 2021 |
| Geodesics of learned representations OJ Hénaff, EP Simoncelli International Conference on Learning Representations (ICLR) 2016, 2015 | 49 | 2015 |
| Object discovery and representation networks OJ Hénaff, S Koppula, E Shelhamer, D Zoran, A Jaegle, A Zisserman, ... European Conference on Computer Vision, 123-143, 2022 | 46 | 2022 |
| Representation of visual uncertainty through neural gain variability OJ Hénaff, ZM Boundy-Singer, K Meding, CM Ziemba, RLT Goris Nature communications 11 (1), 2513, 2020 | 38 | 2020 |
| Primary visual cortex straightens natural video trajectories OJ Hénaff, Y Bai, JA Charlton, I Nauhaus, EP Simoncelli, RLT Goris Nature communications 12 (1), 5982, 2021 | 28 | 2021 |
| HiP: Hierarchical Perceiver J Carreira, S Koppula, D Zoran, A Recasens, C Ionescu, O Henaff, ... arXiv preprint arXiv:2202.10890, 2022 | 16 | 2022 |
| The local low-dimensionality of natural images OJ Hénaff, J Ballé, NC Rabinowitz, EP Simoncelli International Conference on Learning Representations (ICLR) 2015, 2014 | 13 | 2014 |
| Self-supervised video pretraining yields robust and more human-aligned visual representations N Parthasarathy, SMA Eslami, J Carreira, OJ Henaff Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 10* | 2023 |
| Three ways to improve feature alignment for open vocabulary detection R Arandjelović, A Andonian, A Mensch, OJ Hénaff, JB Alayrac, ... arXiv preprint arXiv:2303.13518, 2023 | 6 | 2023 |
| Where should i spend my flops? efficiency evaluations of visual pre-training methods S Koppula, Y Li, E Shelhamer, A Jaegle, N Parthasarathy, R Arandjelovic, ... arXiv preprint arXiv:2209.15589, 2022 | 5 | 2022 |
| Towards In-context Scene Understanding I Balažević, D Steiner, N Parthasarathy, R Arandjelović, OJ Hénaff arXiv preprint arXiv:2306.01667, 2023 | 4 | 2023 |
| Testing a mechanism for temporal prediction in perceptual, neural, and machine representations OJ Hénaff New York University, 2018 | 2 | 2018 |
| The bottleneck in human letter recognition: A computational model AJ Ziskind, O Hénaff, Y LeCun, DG Pelli Vision Sciences Society, 16-21, 2014 | 2 | 2014 |
| Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model K Roth, L Thede, AS Koepke, O Vinyals, O Hénaff, Z Akata arXiv preprint arXiv:2310.17653, 2023 | | 2023 |
| How does V1 population activity inform perceptual certainty? ZM Boundy-Singer, CM Ziemba, OJ Henaff, RLT Goris bioRxiv, 2023.09. 08.556926, 2023 | | 2023 |
João CarreiraGoogle DeepMind在 google.com 的电子邮件经过验证
