| Mixed precision training P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ... arXiv preprint arXiv:1710.03740, 2017 | 1920 | 2017 |
| An introduction to computational networks and the computational network toolkit A Agarwal, E Akchurin, C Basoglu, G Chen, S Cyphers, J Droppo, ... Tech. Rep. MSR-TR-2014-112, 2014 | 482* | 2014 |
| Topological network alignment uncovers biological function and phylogeny O Kuchaiev, T Milenković, V Memišević, W Hayes, N Pržulj Journal of the Royal Society Interface 7 (50), 1341-1354, 2010 | 479 | 2010 |
| An introduction to computational networks and the computational network toolkit D Yu, A Eversole, M Seltzer, K Yao, Z Huang, B Guenter, O Kuchaiev, ... Microsoft Technical Report MSR-TR-2014–112, 2014 | 475 | 2014 |
| 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 | 342 | 2020 |
| Integrative network alignment reveals large regions of global network similarity in yeast and human O Kuchaiev, N Pržulj Bioinformatics 27 (10), 1390-1396, 2011 | 342 | 2011 |
| 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 | 288 | 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 | 268 | 2019 |
| Geometric de-noising of protein-protein interaction networks O Kuchaiev, M Rašajski, DJ Higham, N Pržulj PLoS computational biology 5 (8), e1000454, 2009 | 203 | 2009 |
| Factorization tricks for LSTM networks O Kuchaiev, B Ginsburg arXiv preprint arXiv:1703.10722, 2017 | 158 | 2017 |
| Training deep autoencoders for collaborative filtering O Kuchaiev, B Ginsburg arXiv preprint arXiv:1708.01715, 2017 | 130 | 2017 |
| 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 | 103 | 2019 |
| GraphCrunch 2: software tool for network modeling, alignment and clustering O Kuchaiev, A Stevanović, W Hayes, N Pržulj BMC bioinformatics 12, 1-13, 2011 | 96 | 2011 |
| Geometric evolutionary dynamics of protein interaction networks N Pržulj, O Kuchaiev, A Stevanović, W Hayes Biocomputing 2010, 178-189, 2010 | 69 | 2010 |
| Mixed-precision training for nlp and speech recognition with openseq2seq O Kuchaiev, B Ginsburg, I Gitman, V Lavrukhin, J Li, H Nguyen, C Case, ... arXiv preprint arXiv:1805.10387, 2018 | 51 | 2018 |
| Shall we pretrain autoregressive language models with retrieval? a comprehensive study B Wang, W Ping, P Xu, L McAfee, Z Liu, M Shoeybi, Y Dong, O Kuchaiev, ... arXiv preprint arXiv:2304.06762, 2023 | 49 | 2023 |
| Mixed precision training. arXiv 2017 P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D García, ... arXiv preprint arXiv:1710.03740, 2017 | 49 | 2017 |
| Openseq2seq: extensible toolkit for distributed and mixed precision training of sequence-to-sequence models O Kuchaiev, B Ginsburg, I Gitman, V Lavrukhin, C Case, P Micikevicius Proceedings of Workshop for NLP Open Source Software (NLP-OSS), 41-46, 2018 | 43 | 2018 |
| Spgispeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition PK O'Neill, V Lavrukhin, S Majumdar, V Noroozi, Y Zhang, O Kuchaiev, ... arXiv preprint arXiv:2104.02014, 2021 | 42 | 2021 |
| Learning the structure of protein-protein interaction networks O Kuchaiev, N PRŽULJ Biocomputing 2009, 39-50, 2009 | 29 | 2009 |
Natasa Przulj, PhD, FBCS, MAEICREA Research Professor at Barcelona Supercomputing Center; Professor, Computer Science, UCLVerified email at bsc.es
