| Pixeldefend: Leveraging generative models to understand and defend against adversarial examples Y Song, T Kim, S Nowozin, S Ermon, N Kushman arXiv preprint arXiv:1710.10766, 2017 | 682 | 2017 |
| Learning to Automatically Solve Algebra Word Problems N Kushman, Y Artzi, L Zettlemoyer, R Barzilay | 322 | 2014 |
| Learning to solve arithmetic word problems with verb categorization. MJ Hosseini, H Hajishirzi, O Etzioni, N Kushman EMNLP, 523-533, 2014 | 244 | 2014 |
| R-BGP: Staying connected in a connected world N Kushman, S Kandula, D Katabi, BM Maggs Carnegie Mellon University, 2007 | 236 | 2007 |
| Constructing unrestricted adversarial examples with generative models Y Song, R Shu, N Kushman, S Ermon Advances in Neural Information Processing Systems 31, 2018 | 235 | 2018 |
| Learning to share: narrowband-friendly wideband networks H Rahul, N Kushman, D Katabi, C Sodini, F Edalat ACM SIGCOMM Computer Communication Review 38 (4), 147-158, 2008 | 195 | 2008 |
| ZipTx: Harnessing partial packets in 802.11 networks KCJ Lin, N Kushman, D Katabi Proceedings of the 14th ACM international conference on Mobile computing and …, 2008 | 147 | 2008 |
| Competition-level code generation with alphacode Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ... Science 378 (6624), 1092-1097, 2022 | 141* | 2022 |
| Using semantic unification to generate regular expressions from natural language N Kushman, R Barzilay North American Chapter of the Association for Computational Linguistics (NAACL), 2013 | 140 | 2013 |
| MAWPS: A math word problem repository R Koncel-Kedziorski, S Roy, A Amini, N Kushman, H Hajishirzi Proceedings of the 2016 conference of the north american chapter of the …, 2016 | 128* | 2016 |
| Can you hear me now?! it must be BGP N Kushman, S Kandula, D Katabi ACM SIGCOMM Computer Communication Review 37 (2), 75-84, 2007 | 128 | 2007 |
| Terpret: A probabilistic programming language for program induction AL Gaunt, M Brockschmidt, R Singh, N Kushman, P Kohli, J Taylor, ... arXiv preprint arXiv:1608.04428, 2016 | 119 | 2016 |
| Learning robust representations via multi-view information bottleneck M Federici, A Dutta, P Forré, N Kushman, Z Akata arXiv preprint arXiv:2002.07017, 2020 | 117 | 2020 |
| Neural generation of regular expressions from natural language with minimal domain knowledge N Locascio, K Narasimhan, E DeLeon, N Kushman, R Barzilay arXiv preprint arXiv:1608.03000, 2016 | 85 | 2016 |
| Learning high-level planning from text SRK Branavan, N Kushman, T Lei, R Barzilay The Association for Computational Linguistics, 2012 | 77 | 2012 |
| Differentiable programs with neural libraries AL Gaunt, M Brockschmidt, N Kushman, D Tarlow International Conference on Machine Learning, 1213-1222, 2017 | 58 | 2017 |
| A deep learning approach for joint video frame and reward prediction in atari games F Leibfried, N Kushman, K Hofmann arXiv preprint arXiv:1611.07078, 2016 | 53 | 2016 |
| Neural program lattices C Li, D Tarlow, AL Gaunt, M Brockschmidt, N Kushman International conference on learning representations, 2017 | 31 | 2017 |
| The mutual autoencoder: Controlling information in latent code representations, 2018 M Phuong, M Welling, N Kushman, R Tomioka, S Nowozin URL https://openreview. net/forum, 0 | 27 | |
| Inverse graphics gan: Learning to generate 3d shapes from unstructured 2d data S Lunz, Y Li, A Fitzgibbon, N Kushman arXiv preprint arXiv:2002.12674, 2020 | 25 | 2020 |
Dina KatabiProfessor of EECS, MITVerified email at ai.mit.edu
