| End-To-End Memory Networks S Sukhbaatar, A Szlam, J Weston, R Fergus | 2342 | 2015 |
| Learning multiagent communication with backpropagation S Sukhbaatar, A Szlam, R Fergus Advances in Neural Information Processing Systems, 2244-2252, 2016 | 683 | 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 | 552* | 2014 |
| Simple baseline for visual question answering B Zhou, Y Tian, S Sukhbaatar, A Szlam, R Fergus arXiv preprint arXiv:1512.02167, 2015 | 292 | 2015 |
| 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 | 200 | 2017 |
| Adaptive attention span in transformers S Sukhbaatar, E Grave, P Bojanowski, A Joulin arXiv preprint arXiv:1905.07799, 2019 | 121 | 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 | 83 | 2018 |
| Mazebase: A sandbox for learning from games S Sukhbaatar, A Szlam, G Synnaeve, S Chintala, R Fergus arXiv preprint arXiv:1511.07401, 2015 | 71 | 2015 |
| Composable planning with attributes A Zhang, S Sukhbaatar, A Lerer, A Szlam, R Fergus International Conference on Machine Learning, 5842-5851, 2018 | 48 | 2018 |
| Augmenting self-attention with persistent memory S Sukhbaatar, E Grave, G Lample, H Jegou, A Joulin arXiv preprint arXiv:1907.01470, 2019 | 36 | 2019 |
| Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Nets S Sukhbaatar, T Makino, K Aihara, T Chikayama Asian Conference on Machine Learning, 231--246, 2011 | 20 | 2011 |
| End-to-end memory networks JE Weston, AD Szlam, RD Fergus, S Sukhbaatar US Patent 10,664,744, 2020 | 19 | 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 | 17 | 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 | 13* | 2020 |
| Learning to visually navigate in photorealistic environments without any supervision L Mezghani, S Sukhbaatar, A Szlam, A Joulin, P Bojanowski arXiv preprint arXiv:2004.04954, 2020 | 7 | 2020 |
| Auto-pooling: Learning to improve invariance of image features from image sequences S Sukhbaatar, T Makino, K Aihara arXiv preprint arXiv:1301.3323, 2013 | 6 | 2013 |
| Training hybrid language models by marginalizing over segmentations E Grave, S Sukhbaatar, P Bojanowski, A Joulin Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 5 | 2019 |
| Hash Layers For Large Sparse Models S Roller, S Sukhbaatar, A Szlam, J Weston arXiv preprint arXiv:2106.04426, 2021 | 4 | 2021 |
| 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 arXiv preprint arXiv:2105.06548, 2021 | 2 | 2021 |
| Memory-Augmented Reinforcement Learning for Image-Goal Navigation L Mezghani, S Sukhbaatar, T Lavril, O Maksymets, D Batra, P Bojanowski, ... arXiv preprint arXiv:2101.05181, 2021 | 2 | 2021 |
Rob FergusResearch Scientist, DeepMind. Professor of Computer Science, New York UniversityVerified email at cs.nyu.edu
