| Rectifier nonlinearities improve neural network acoustic models AL Maas, AY Hannun, AY Ng Proc. icml 30 (1), 3, 2013 | 8924 | 2013 |
| 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 | 3612 | 2016 |
| Deep speech: Scaling up end-to-end speech recognition A Hannun, C Case, J Casper, B Catanzaro, G Diamos, E Elsen, ... arXiv preprint arXiv:1412.5567, 2014 | 2586 | 2014 |
| Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network AY Hannun, P Rajpurkar, M Haghpanahi, GH Tison, C Bourn, ... Nature medicine 25 (1), 65-69, 2019 | 2262 | 2019 |
| Cardiologist-level arrhythmia detection with convolutional neural networks P Rajpurkar, AY Hannun, M Haghpanahi, C Bourn, AY Ng arXiv preprint arXiv:1707.01836, 2017 | 936 | 2017 |
| Rectifier nonlinearities improve neural network acoustic models AL Mass, AY Hannun, AY Ng International Conference on Machine Learning (ICML) 28, 1, 2013 | 284 | 2013 |
| Certified data removal from machine learning models C Guo, T Goldstein, A Hannun, L Van Der Maaten arXiv preprint arXiv:1911.03030, 2019 | 279 | 2019 |
| 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 | 276 | 2021 |
| Gradient matching for domain generalization Y Shi, J Seely, PHS Torr, N Siddharth, A Hannun, N Usunier, G Synnaeve arXiv preprint arXiv:2104.09937, 2021 | 232 | 2021 |
| Self-training for end-to-end speech recognition J Kahn, A Lee, A Hannun ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 230 | 2020 |
| Wav2letter++: A fast open-source speech recognition system V Pratap, A Hannun, Q Xu, J Cai, J Kahn, G Synnaeve, V Liptchinsky, ... ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 220 | 2019 |
| First-pass large vocabulary continuous speech recognition using bi-directional recurrent dnns AY Hannun, AL Maas, D Jurafsky, AY Ng arXiv preprint arXiv:1408.2873, 2014 | 205 | 2014 |
| Building DNN acoustic models for large vocabulary speech recognition AL Maas, P Qi, Z Xie, AY Hannun, CT Lengerich, D Jurafsky, AY Ng Computer Speech & Language 41, 195-213, 2017 | 181 | 2017 |
| Sequence modeling with ctc A Hannun Distill 2 (11), e8, 2017 | 141* | 2017 |
| Massively multilingual ASR: 50 languages, 1 model, 1 billion parameters V Pratap, A Sriram, P Tomasello, A Hannun, V Liptchinsky, G Synnaeve, ... arXiv preprint arXiv:2007.03001, 2020 | 136 | 2020 |
| Iterative pseudo-labeling for speech recognition Q Xu, T Likhomanenko, J Kahn, A Hannun, G Synnaeve, R Collobert arXiv preprint arXiv:2005.09267, 2020 | 126 | 2020 |
| Deployed end-to-end speech recognition B Catanzaro, J Chen, M Chrzanowski, E Elsen, J Engel, C Fougner, ... US Patent App. 15/358,083, 2017 | 122 | 2017 |
| 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 | 114 | 2016 |
| Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions A Hannun, A Lee, Q Xu, R Collobert arXiv preprint arXiv:1904.02619, 2019 | 106 | 2019 |
| End-to-end speech recognition B Catanzaro, J Chen, M Chrzanowski, E Elsen, J Engel, C Fougner, ... US Patent 10,332,509, 2019 | 89 | 2019 |
Andrew NgStanford UniversityVerified email at cs.stanford.edu
