| Improving language understanding by generative pre-training A Radford, K Narasimhan, T Salimans, I Sutskever URL https://s3-us-west-2. amazonaws. com/openai-assets/research-covers …, 2018 | 1725 | 2018 |
| Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation TD Kulkarni, KR Narasimhan, A Saeedi, JB Tenenbaum Neural Information Processing Systems (NIPS), 2016 | 659 | 2016 |
| Language understanding for text-based games using deep reinforcement learning K Narasimhan, T Kulkarni, R Barzilay Empirical Methods in Natural Language Processing (EMNLP), 2015 | 283 | 2015 |
| Improving language understanding with unsupervised learning A Radford, K Narasimhan, T Salimans, I Sutskever Technical report, OpenAI, 2018 | 257 | 2018 |
| Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning K Narasimhan, A Yala, R Barzilay Empirical Methods in Natural Language Processing (EMNLP), 2016 | 115 | 2016 |
| Nonparametric Spherical Topic Modeling with Word Embeddings K Batmanghelich, A Saeedi, K Narasimhan, S Gershman Association for Computational Linguistics (ACL), 2016 | 72 | 2016 |
| An Unsupervised Method for Uncovering Morphological Chains K Narasimhan, R Barzilay, T Jaakkola Transactions of the Association for Computational Linguistics (TACL), 2015 | 55 | 2015 |
| Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge N Locascio, K Narasimhan, E DeLeon, N Kushman, R Barzilay Empirical Methods in Natural Language Processing (EMNLP), 2016 | 51 | 2016 |
| Machine Comprehension with Discourse Relations K Narasimhan, R Barzilay Association for Computational Linguistics (ACL), 2015 | 50 | 2015 |
| sk_p: a neural program corrector for MOOCs Y Pu, K Narasimhan, A Solar-Lezama, R Barzilay Companion Proceedings of the 2016 ACM SIGPLAN International Conference on …, 2016 | 49 | 2016 |
| Improving Language Understanding by Generative Pre A Radford, K Narasimhan, T Salimans, I Sutskever Training 12, 2018 | 38 | 2018 |
| Grounding language for transfer in deep reinforcement learning K Narasimhan, R Barzilay, T Jaakkola Journal of Artificial Intelligence Research 63, 849-874, 2018 | 34* | 2018 |
| Representation learning for grounded spatial reasoning M Janner, K Narasimhan, R Barzilay Transactions of the Association for Computational Linguistics 6, 49-61, 2018 | 34 | 2018 |
| Morphological Segmentation for Keyword Spotting K Narasimhan, D Karakos, R Schwartz, S Tsakalidis, R Barzilay Empirical Methods in Natural Language Processing (EMNLP), 2014 | 33 | 2014 |
| Task-agnostic dynamics priors for deep reinforcement learning Y Du, K Narasimhan International Conference on Machine Learning, 2019 | 19 | 2019 |
| A generalized algorithm for multi-objective reinforcement learning and policy adaptation R Yang, X Sun, K Narasimhan Advances in Neural Information Processing Systems, 14636-14647, 2019 | 15 | 2019 |
| Projection-Based Constrained Policy Optimization. TY Yang, J Rosca, K Narasimhan, PJ Ramadge International Conference on Learning Representations, 2020 | 10 | 2020 |
| Unsupervised Learning of Morphological Forests J Luo, K Narasimhan, R Barzilay Transactions of the Association for Computational Linguistics (TACL), 2017 | 9 | 2017 |
| Constructing sub-word units for spoken term detection C Van Heerden, D Karakos, K Narasimhan, M Davel, R Schwartz 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 8 | 2017 |
| JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes JH Huggins, K Narasimhan, A Saeedi, VK Mansinghka International Conference on Machine Learning (ICML), 2015 | 8 | 2015 |
Tim SalimansGoogle Brain AmsterdamVerified email at google.com
