| Federated learning for mobile keyboard prediction A Hard, K Rao, R Mathews, S Ramaswamy, F Beaufays, S Augenstein, ... arXiv preprint arXiv:1811.03604, 2018 | 1782 | 2018 |
| State-of-the-art speech recognition with sequence-to-sequence models CC Chiu, TN Sainath, Y Wu, R Prabhavalkar, P Nguyen, Z Chen, ... 2018 IEEE international conference on acoustics, speech and signal …, 2018 | 1474 | 2018 |
| Do as i can, not as i say: Grounding language in robotic affordances M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, C Fu, ... arXiv preprint arXiv:2204.01691, 2022 | 1356 | 2022 |
| Rt-1: Robotics transformer for real-world control at scale A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ... arXiv preprint arXiv:2212.06817, 2022 | 787 | 2022 |
| Streaming end-to-end speech recognition for mobile devices Y He, TN Sainath, R Prabhavalkar, I McGraw, R Alvarez, D Zhao, ... ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 748 | 2019 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control A Brohan, N Brown, J Carbajal, Y Chebotar, X Chen, K Choromanski, ... arXiv preprint arXiv:2307.15818, 2023 | 644 | 2023 |
| Fast and accurate recurrent neural network acoustic models for speech recognition H Sak, A Senior, K Rao, F Beaufays Interspeech 2015, 2015 | 558 | 2015 |
| Do as i can, not as i say: Grounding language in robotic affordances A Brohan, Y Chebotar, C Finn, K Hausman, A Herzog, D Ho, J Ibarz, ... Conference on robot learning, 287-318, 2023 | 424 | 2023 |
| Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer K Rao, H Sak, R Prabhavalkar 2017 IEEE automatic speech recognition and understanding workshop (ASRU …, 2017 | 414 | 2017 |
| A Comparison of sequence-to-sequence models for speech recognition. R Prabhavalkar, K Rao, TN Sainath, B Li, L Johnson, N Jaitly Interspeech, 939-943, 2017 | 397 | 2017 |
| Federated learning for emoji prediction in a mobile keyboard S Ramaswamy, R Mathews, K Rao, F Beaufays arXiv preprint arXiv:1906.04329, 2019 | 356 | 2019 |
| Multilingual speech recognition with a single end-to-end model S Toshniwal, TN Sainath, RJ Weiss, B Li, P Moreno, E Weinstein, K Rao 2018 IEEE international conference on acoustics, speech and signal …, 2018 | 297 | 2018 |
| Grapheme-to-phoneme conversion using long short-term memory recurrent neural networks K Rao, F Peng, H Sak, F Beaufays 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 284 | 2015 |
| Open x-embodiment: Robotic learning datasets and rt-x models A O'Neill, A Rehman, A Gupta, A Maddukuri, A Gupta, A Padalkar, A Lee, ... arXiv preprint arXiv:2310.08864, 2023 | 279 | 2023 |
| Learning acoustic frame labeling for speech recognition with recurrent neural networks H Sak, A Senior, K Rao, O Irsoy, A Graves, F Beaufays, J Schalkwyk 2015 IEEE international conference on acoustics, speech and signal …, 2015 | 230 | 2015 |
| Personalized speech recognition on mobile devices I McGraw, R Prabhavalkar, R Alvarez, MG Arenas, K Rao, D Rybach, ... 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 229 | 2016 |
| Lingvo: a modular and scalable framework for sequence-to-sequence modeling J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ... arXiv preprint arXiv:1902.08295, 2019 | 214 | 2019 |
| Rl-cyclegan: Reinforcement learning aware simulation-to-real K Rao, C Harris, A Irpan, S Levine, J Ibarz, M Khansari Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 207 | 2020 |
| Large-scale visual speech recognition B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ... arXiv preprint arXiv:1807.05162, 2018 | 207 | 2018 |
| Open-vocabulary queryable scene representations for real world planning B Chen, F Xia, B Ichter, K Rao, K Gopalakrishnan, MS Ryoo, A Stone, ... 2023 IEEE International Conference on Robotics and Automation (ICRA), 11509 …, 2023 | 174 | 2023 |