| Rectifier nonlinearities improve neural network acoustic models AL Maas, AY Hannun, AY Ng Proc. icml 30 (1), 3, 2013 | 4285 | 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 | 1939 | 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 | 1365 | 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 | 626 | 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 | 461 | 2017 |
| 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 | 119 | 2014 |
| 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 | 84 | 2019 |
| 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 | 76 | 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 | 72 | 2016 |
| Learning Multiscale Features Directly from Waveforms Z Zhu, JH Engel, A Hannun Interspeech 2016, 1305-1309, 2016 | 47 | 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 | 43 | 2019 |
| Sequence modeling with ctc A Hannun Distill 2 (11), e8, 2017 | 39 | 2017 |
| 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 | 37 | 2017 |
| Systems and methods for speech transcription A Hannun, C Case, J Casper, B Catanzaro, G Diamos, E Elsen, ... US Patent 10,540,957, 2020 | 35 | 2020 |
| 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 | 31 | 2020 |
| An end-to-end architecture for keyword spotting and voice activity detection C Lengerich, A Hannun arXiv preprint arXiv:1611.09405, 2016 | 30 | 2016 |
| Unsupervised feature learning and deep learning A Ng, J Ngiam, CY Foo, Y Mai, C Suen, A Coates, A Maas, A Hannun, ... Technical report, Stanford University, 2013 | 26 | 2013 |
| Recurrent neural network feature enhancement: The 2nd CHiME challenge AL Maas, TM O’Neil, AY Hannun, AY Ng Proceedings The 2nd CHiME Workshop on Machine Listening in Multisource …, 2013 | 24 | 2013 |
| Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning Y Shen, M Voisin, A Aliamiri, A Avati, A Hannun, A Ng Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 21 | 2019 |
| Certified data removal from machine learning models C Guo, T Goldstein, A Hannun, L Van Der Maaten arXiv preprint arXiv:1911.03030, 2019 | 20 | 2019 |
Andrew NgStanford UniversityVerified email at cs.stanford.edu
