| Mixed precision training P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ... arXiv preprint arXiv:1710.03740, 2017 | 2118 | 2017 |
| Scaling SGD Batch Size to 32K for ImageNet Training Y You, I Gitman, B Ginsburg arXiv preprint arXiv:1708.03888, 2017 | 1307* | 2017 |
| Quartznet: Deep automatic speech recognition with 1d time-channel separable convolutions S Kriman, S Beliaev, B Ginsburg, J Huang, O Kuchaiev, V Lavrukhin, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 369 | 2020 |
| Jasper: An End-to-End Convolutional Neural Acoustic Model J Li, V Lavrukhin, B Ginsburg, R Leary, O Kuchaiev, JM Cohen, H Nguyen, ... arXiv preprint arXiv:1904.03288, 2019 | 308 | 2019 |
| NeMo: a toolkit for building AI applications using Neural Modules O Kuchaiev, J Li, H Nguyen, O Hrinchuk, R Leary, B Ginsburg, S Kriman, ... arXiv preprint arXiv:1909.09577, 2019 | 295 | 2019 |
| Method for power saving in a wireless LAN B Ginzburg, O Kaidar, V Kondratiev US Patent 20040264396A1, 2003 | 262 | 2003 |
| The ForSpec temporal logic: A new temporal property-specification language R Armoni, L Fix, A Flaisher, R Gerth, B Ginsburg, T Kanza, A Landver, ... International Conference on Tools and Algorithms for the Construction and …, 2002 | 256 | 2002 |
| BigVGAN: A Universal Neural Vocoder with Large-Scale Training S Lee, W Ping, B Ginsburg, B Catanzaro, S Yoon arXiv preprint arXiv:2206.04658, 2022 | 227 | 2022 |
| Performance analysis of A-MPDU and A-MSDU aggregation in IEEE 802.11 n B Ginzburg, A Kesselman 2007 IEEE Sarnoff Symposium, 1-5, 2007 | 190 | 2007 |
| Factorization tricks for LSTM networks O Kuchaiev, B Ginsburg ICLR 2017 Workshop, 2017 | 169 | 2017 |
| Multiplying and adding matrices B Ginzburg, S Rubanovich, B Eitan US Patent 8,984,043, 2015 | 157 | 2015 |
| Training Deep AutoEncoders for Collaborative Filtering O Kuchaiev, B Ginsburg arXiv preprint arXiv:1708.01715, 2017 | 140 | 2017 |
| Method and apparatus of multi-entity wireless communication adapter Y Bachrach, A Barak, B Ginzburg US Patent 7,656,892, 2010 | 137* | 2010 |
| Stochastic gradient methods with layer-wise adaptive moments for training of deep networks B Ginsburg, P Castonguay, O Hrinchuk, O Kuchaiev, V Lavrukhin, R Leary, ... arXiv preprint arXiv:1905.11286, 2019 | 124 | 2019 |
| Computational mammography using deep neural networks A Dubrovina, P Kisilev, B Ginsburg, S Hashoul, R KImmel Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2016 | 120 | 2016 |
| TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context NR Koluguri, T Park, B Ginsburg ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 119 | 2022 |
| Reliable multicast techniques for wireless links B Ginzburg, M Fudim US Patent 20080002621A1, 2006 | 112 | 2006 |
| Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model O Hrinchuk, M Popova, B Ginsburg ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 107 | 2020 |
| Hi-Fi Multi-Speaker English TTS Dataset E Bakhturina, V Lavrukhin, B Ginsburg, Y Zhang arXiv preprint arXiv:2104.01497, 2021 | 106 | 2021 |
| MatchboxNet--1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition S Majumdar, B Ginsburg arXiv preprint arXiv:2004.08531, 2020 | 102 | 2020 |
Vitaly LavrukhinNVIDIA在 nvidia.com 的电子邮件经过验证