| Adversarial policies: Attacking deep reinforcement learning A Gleave, M Dennis, C Wild, N Kant, S Levine, S Russell arXiv preprint arXiv:1905.10615, 2019 | 645 | 2019 |
| Helpsteer: Multi-attribute helpfulness dataset for steerlm Z Wang, Y Dong, J Zeng, V Adams, MN Sreedhar, D Egert, O Delalleau, ... Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 205 | 2024 |
| End-to-end training of neural retrievers for open-domain question answering D Sachan, M Patwary, M Shoeybi, N Kant, W Ping, WL Hamilton, ... Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 134 | 2021 |
| Practical text classification with large pre-trained language models N Kant, R Puri, N Yakovenko, B Catanzaro arXiv preprint arXiv:1812.01207, 2018 | 118 | 2018 |
| PrefixRL: Optimization of parallel prefix circuits using deep reinforcement learning R Roy, J Raiman, N Kant, I Elkin, R Kirby, M Siu, S Oberman, S Godil, ... 2021 58th ACM/IEEE Design Automation Conference (DAC), 853-858, 2021 | 103 | 2021 |
| Polaris: A safety-focused llm constellation architecture for healthcare S Mukherjee, P Gamble, MS Ausin, N Kant, K Aggarwal, N Manjunath, ... arXiv preprint arXiv:2403.13313, 2024 | 88 | 2024 |
| Synthetic datasets for neural program synthesis R Shin, N Kant, K Gupta, C Bender, B Trabucco, R Singh, D Song arXiv preprint arXiv:1912.12345, 2019 | 58 | 2019 |
| Recent advances in neural program synthesis N Kant arXiv preprint arXiv:1802.02353, 2018 | 31 | 2018 |
| The road to general intelligence J Swan, E Nivel, N Kant, J Hedges, T Atkinson, B Steunebrink Springer Nature, 2022 | 18 | 2022 |
| Neural Inference of API Functions from Input–Output Examples R Bavishi, C Lemieux, N Kant, R Fox, K Sen, I Stoica Workshop on ML for Systems at NeurIPS, 2018 | 2 | 2018 |
Bryan CatanzaroNVIDIAVerified email at acm.org
