| Deep speech 2: End-to-end speech recognition in english and mandarin D Amodei, S Ananthanarayanan, R Anubhai, J Bai, E Battenberg, C Case, ... International conference on machine learning, 173-182, 2016 | 3834 | 2016 |
| Deep Speech: Scaling up end-to-end speech recognition A Hannun arXiv preprint arXiv:1412.5567, 2014 | 2742 | 2014 |
| Parallel prefix sum (scan) with cuda M Harris GPU Gems 3, 2007 | 1097 | 2007 |
| Scan primitives for GPU computing S Sengupta, M Harris, Y Zhang, JD Owens | 856 | 2007 |
| Deep voice: Real-time neural text-to-speech SÖ Arık, M Chrzanowski, A Coates, G Diamos, A Gibiansky, Y Kang, X Li, ... International conference on machine learning, 195-204, 2017 | 842 | 2017 |
| Fast BVH construction on GPUs C Lauterbach, M Garland, S Sengupta, D Luebke, D Manocha Computer Graphics Forum 28 (2), 375-384, 2009 | 620 | 2009 |
| Crypten: Secure multi-party computation meets machine learning B Knott, S Venkataraman, A Hannun, S Sengupta, M Ibrahim, ... Advances in Neural Information Processing Systems 34, 4961-4973, 2021 | 372 | 2021 |
| Exploring sparsity in recurrent neural networks S Narang, E Elsen, G Diamos, S Sengupta arXiv preprint arXiv:1704.05119, 2017 | 372 | 2017 |
| Real-time parallel hashing on the GPU DA Alcantara, A Sharf, F Abbasinejad, S Sengupta, M Mitzenmacher, ... ACM SIGGRAPH asia 2009 papers, 1-9, 2009 | 271 | 2009 |
| Large language models for software engineering: Survey and open problems A Fan, B Gokkaya, M Harman, M Lyubarskiy, S Sengupta, S Yoo, ... 2023 IEEE/ACM International Conference on Software Engineering: Future of …, 2023 | 270 | 2023 |
| Efficient parallel scan algorithms for many-core gpus S Sengupta, MJ Harris, M Garland, JD Owens eScholarship, University of California, 2011 | 259 | 2011 |
| Navigating the maze of graph analytics frameworks using massive graph datasets N Satish, N Sundaram, MMA Patwary, J Seo, J Park, MA Hassaan, ... Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014 | 246 | 2014 |
| Glift: Generic, efficient, random-access GPU data structures AE Lefohn, S Sengupta, J Kniss, R Strzodka, JD Owens ACM Transactions on Graphics (TOG) 25 (1), 60-99, 2006 | 233 | 2006 |
| Elf opengo: An analysis and open reimplementation of alphazero Y Tian, J Ma, Q Gong, S Sengupta, Z Chen, J Pinkerton, L Zitnick International conference on machine learning, 6244-6253, 2019 | 133 | 2019 |
| A work-efficient step-efficient prefix sum algorithm S Sengupta, A Lefohn, JD Owens | 124 | 2006 |
| Persistent rnns: Stashing recurrent weights on-chip G Diamos, S Sengupta, B Catanzaro, M Chrzanowski, A Coates, E Elsen, ... International Conference on Machine Learning, 2024-2033, 2016 | 121 | 2016 |
| Resolution-matched shadow maps AE Lefohn, S Sengupta, JD Owens ACM Transactions on Graphics (TOG) 26 (4), 20-es, 2007 | 97 | 2007 |
| Building an efficient hash table on the GPU DA Alcantara, V Volkov, S Sengupta, M Mitzenmacher, JD Owens, ... GPU Computing Gems Jade Edition, 39-53, 2012 | 86 | 2012 |
| Out‐of‐core data management for path tracing on hybrid resources B Budge, T Bernardin, JA Stuart, S Sengupta, KI Joy, JD Owens Computer Graphics Forum 28 (2), 385-396, 2009 | 70 | 2009 |
| CUDPP: CUDA data parallel primitives library M Harris, J Owens, S Sengupta, Y Zhang, A Davidson 2015-04-05]. http://code. google. com/p/cudpp, 2007 | 66 | 2007 |