| End-to-end memory networks S Sukhbaatar, A Szlam, J Weston, R Fergus Advances in neural information processing systems 28, 2015 | 1741 | 2015 |
| 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 | 1539 | 2015 |
| Spectral networks and locally connected networks on graphs J Bruna, W Zaremba, A Szlam, Y LeCun arXiv preprint arXiv:1312.6203, 2013 | 1458 | 2013 |
| 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 | 1021 | 2017 |
| Learning multiagent communication with backpropagation S Sukhbaatar, A Szlam, R Fergus Advances in Neural Information Processing Systems, 2244-2252, 2016 | 429 | 2016 |
| 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 | 339 | 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 | 337 | 2010 |
| 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 | 299 | 2014 |
| 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 | 270 | 2018 |
| Simple baseline for visual question answering B Zhou, Y Tian, S Sukhbaatar, A Szlam, R Fergus arXiv preprint arXiv:1512.02167, 2015 | 216 | 2015 |
| 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 | 206 | 2012 |
| 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 | 166 | 2009 |
| Regularization on graphs with function-adapted diffusion processes AD Szlam, M Maggioni, RR Coifman Journal of Machine Learning Research 9 (Aug), 1711-1739, 2008 | 160 | 2008 |
| 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 | 147 | 2015 |
| Tracking the world state with recurrent entity networks M Henaff, J Weston, A Szlam, A Bordes, Y LeCun arXiv preprint arXiv:1612.03969, 2016 | 144 | 2016 |
| Optimizing the latent space of generative networks P Bojanowski, A Joulin, D Lopez-Paz, A Szlam arXiv preprint arXiv:1707.05776, 2017 | 133 | 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 | 131 | 2017 |
| Total Variation, Cheeger Cuts. A Szlam, X Bresson ICML, 1039-1046, 2010 | 100 | 2010 |
| Diffusion wavelet packets JC Bremer, RR Coifman, M Maggioni, AD Szlam Applied and Computational Harmonic Analysis 21 (1), 95-112, 2006 | 68 | 2006 |
| Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions AD Szlam, M Maggioni, RR Coifman, JC Bremer Jr Wavelets XI 5914, 59141D, 2005 | 67 | 2005 |
