| Spectral networks and locally connected networks on graphs J Bruna, W Zaremba, A Szlam, Y LeCun arXiv preprint arXiv:1312.6203, 2013 | 3600 | 2013 |
| End-to-end memory networks S Sukhbaatar, A Szlam, J Weston, R Fergus Advances in neural information processing systems 28, 2015 | 2484 | 2015 |
| Geometric deep learning: going beyond euclidean data MM Bronstein, J Bruna, Y LeCun, A Szlam, P Vandergheynst IEEE Signal Processing Magazine 34 (4), 18-42, 2017 | 2378 | 2017 |
| Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks E Denton, S Chintala, A Szlam, R Fergus Advances in neural information processing systems 28, 2015 | 2301 | 2015 |
| Personalizing dialogue agents: I have a dog, do you have pets too? S Zhang, E Dinan, J Urbanek, A Szlam, D Kiela, J Weston arXiv preprint arXiv:1801.07243, 2018 | 810 | 2018 |
| Learning multiagent communication with backpropagation S Sukhbaatar, A Szlam, R Fergus Advances in Neural Information Processing Systems, 2244-2252, 2016 | 788 | 2016 |
| Video (language) modeling: a baseline for generative models of natural videos MA Ranzato, A Szlam, J Bruna, M Mathieu, R Collobert, S Chopra arXiv preprint arXiv:1412.6604, 2014 | 423 | 2014 |
| Incremental gradient on the grassmannian for online foreground and background separation in subsampled video J He, L Balzano, A Szlam 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1568-1575, 2012 | 410 | 2012 |
| A randomized algorithm for principal component analysis V Rokhlin, A Szlam, M Tygert SIAM Journal on Matrix Analysis and Applications 31 (3), 1100-1124, 2010 | 408 | 2010 |
| Simple baseline for visual question answering B Zhou, Y Tian, S Sukhbaatar, A Szlam, R Fergus arXiv preprint arXiv:1512.02167, 2015 | 335 | 2015 |
| Optimizing the latent space of generative networks P Bojanowski, A Joulin, D Lopez-Paz, A Szlam arXiv preprint arXiv:1707.05776, 2017 | 319 | 2017 |
| 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 | 250 | 2017 |
| Tracking the world state with recurrent entity networks M Henaff, J Weston, A Szlam, A Bordes, Y LeCun arXiv preprint arXiv:1612.03969, 2016 | 228 | 2016 |
| Hybrid linear modeling via local best-fit flats T Zhang, A Szlam, Y Wang, G Lerman International journal of computer vision 100 (3), 217-240, 2012 | 224 | 2012 |
| The second conversational intelligence challenge (convai2) E Dinan, V Logacheva, V Malykh, A Miller, K Shuster, J Urbanek, D Kiela, ... The NeurIPS'18 Competition, 187-208, 2020 | 220 | 2020 |
| Median k-flats for hybrid linear modeling with many outliers T Zhang, A Szlam, G Lerman 2009 IEEE 12th international conference on computer vision workshops, ICCV …, 2009 | 193 | 2009 |
| Evaluating prerequisite qualities for learning end-to-end dialog systems J Dodge, A Gane, X Zhang, A Bordes, S Chopra, A Miller, A Szlam, ... arXiv preprint arXiv:1511.06931, 2015 | 191 | 2015 |
| Regularization on graphs with function-adapted diffusion processes. AD Szlam, M Maggioni, RR Coifman Journal of Machine Learning Research 9 (8), 2008 | 164 | 2008 |
| Modeling others using oneself in multi-agent reinforcement learning R Raileanu, E Denton, A Szlam, R Fergus International conference on machine learning, 4257-4266, 2018 | 139 | 2018 |
| Recurrent orthogonal networks and long-memory tasks M Henaff, A Szlam, Y LeCun International Conference on Machine Learning, 2034-2042, 2016 | 136 | 2016 |
