| End-To-End Memory Networks S Sukhbaatar, A Szlam, J Weston, R Fergus | 3224 | 2015 |
| Learning multiagent communication with backpropagation S Sukhbaatar, A Szlam, R Fergus Advances in Neural Information Processing Systems, 2244-2252, 2016 | 1317 | 2016 |
| Training Convolutional Networks with Noisy Labels S Sukhbaatar, J Bruna, M Paluri, L Bourdev, R Fergus Accepted as a workshop contribution at ICLR 2015, 2014 | 943* | 2014 |
| Intrinsic motivation and automatic curricula via asymmetric self-play S Sukhbaatar, Z Lin, I Kostrikov, G Synnaeve, A Szlam, R Fergus arXiv preprint arXiv:1703.05407, 2017 | 421 | 2017 |
| Simple baseline for visual question answering B Zhou, Y Tian, S Sukhbaatar, A Szlam, R Fergus arXiv preprint arXiv:1512.02167, 2015 | 412 | 2015 |
| Adaptive attention span in transformers S Sukhbaatar, E Grave, P Bojanowski, A Joulin arXiv preprint arXiv:1905.07799, 2019 | 308 | 2019 |
| Learning when to communicate at scale in multiagent cooperative and competitive tasks A Singh, T Jain, S Sukhbaatar arXiv preprint arXiv:1812.09755, 2018 | 302 | 2018 |
| Hash layers for large sparse models S Roller, S Sukhbaatar, J Weston Advances in Neural Information Processing Systems 34, 17555-17566, 2021 | 156 | 2021 |
| Augmenting self-attention with persistent memory S Sukhbaatar, E Grave, G Lample, H Jegou, A Joulin arXiv preprint arXiv:1907.01470, 2019 | 117 | 2019 |
| Self-rewarding language models W Yuan, RY Pang, K Cho, S Sukhbaatar, J Xu, J Weston arXiv preprint arXiv:2401.10020, 2024 | 114 | 2024 |
| Mazebase: A sandbox for learning from games S Sukhbaatar, A Szlam, G Synnaeve, S Chintala, R Fergus arXiv preprint arXiv:1511.07401, 2015 | 81 | 2015 |
| Composable planning with attributes A Zhang, S Sukhbaatar, A Lerer, A Szlam, R Fergus International Conference on Machine Learning, 5842-5851, 2018 | 76 | 2018 |
| Addressing Some Limitations of Transformers with Feedback Memory A Fan, T Lavril, E Grave, A Joulin, S Sukhbaatar arXiv preprint arXiv:2002.09402, 2020 | 70* | 2020 |
| Learning goal embeddings via self-play for hierarchical reinforcement learning S Sukhbaatar, E Denton, A Szlam, R Fergus arXiv preprint arXiv:1811.09083, 2018 | 59 | 2018 |
| Memory-augmented reinforcement learning for image-goal navigation L Mezghan, S Sukhbaatar, T Lavril, O Maksymets, D Batra, P Bojanowski, ... 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 51 | 2022 |
| End-to-end memory networks JE Weston, AD Szlam, RD Fergus, S Sukhbaatar US Patent 10,664,744, 2020 | 42 | 2020 |
| Not all memories are created equal: Learning to forget by expiring S Sukhbaatar, D Ju, S Poff, S Roller, A Szlam, J Weston, A Fan International Conference on Machine Learning, 9902-9912, 2021 | 38 | 2021 |
| Director: Generator-classifiers for supervised language modeling K Arora, K Shuster, S Sukhbaatar, J Weston arXiv preprint arXiv:2206.07694, 2022 | 32 | 2022 |
| The cringe loss: Learning what language not to model L Adolphs, T Gao, J Xu, K Shuster, S Sukhbaatar, J Weston arXiv preprint arXiv:2211.05826, 2022 | 25 | 2022 |
| Some things are more cringe than others: Preference optimization with the pairwise cringe loss J Xu, A Lee, S Sukhbaatar, J Weston arXiv preprint arXiv:2312.16682, 2023 | 24 | 2023 |
Jason WestonMeta在 fb.com 的电子邮件经过验证
