| TensorFlow: Large-scale machine learning on heterogeneous systems, 2015 M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... Software available from tensorflow.org, 2015 | 27286* | 2015 |
| Large scale distributed deep networks J Dean, GS Corrado, R Monga, K Chen, M Devin, QV Le, MZ Mao, ... | 3204 | 2012 |
| Large scale distributed deep networks J Dean, GS Corrado, R Monga, K Chen, M Devin, QV Le, MZ Mao, ... | 3204 | 2012 |
| Building high-level features using large scale unsupervised learning QV Le, MA Ranzato, R Monga, M Devin, K Chen, GS Corrado, J Dean, ... International Conference on Machine Learning, 2012 | 2475 | 2012 |
| Beyond short snippets: Deep networks for video classification J Yue-Hei Ng, M Hausknecht, S Vijayanarasimhan, O Vinyals, R Monga, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1981 | 2015 |
| On rectified linear units for speech processing MD Zeiler, M Ranzato, R Monga, M Mao, K Yang, QV Le, P Nguyen, ... 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 527 | 2013 |
| Revisiting distributed synchronous SGD J Chen, X Pan, R Monga, S Bengio, R Jozefowicz arXiv preprint arXiv:1604.00981, 2016 | 474 | 2016 |
| TensorFlow: Large-scale machine learning on heterogeneous systems, software available from tensorflow. org (2015) M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... URL https://www. tensorflow. org, 2015 | 314 | 2015 |
| Tensorflow: large-scale machine learning on heterogeneous distributed systems (2016) M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467 172, 2015 | 216 | 2015 |
| Managing controlled content on a web page having revenue-generating code JE Pitkow, D Diklic, R Monga US Patent App. 12/386,362, 2010 | 154 | 2010 |
| Sequence discriminative distributed training of long short-term memory recurrent neural networks H Sak, O Vinyals, G Heigold, A Senior, E McDermott, R Monga, M Mao | 144 | 2014 |
| Google Brain. TensorFlow: A system for large-scale machine learning M Abadi, P Barham, J Chen, Z Chen, A Davis, J Dean, M Devin, ... Proceedings of the 12th USENIX Symposium on Operating Systems Design and …, 2016 | 86 | 2016 |
| Tensorflow. js: Machine learning for the web and beyond D Smilkov, N Thorat, Y Assogba, A Yuan, N Kreeger, P Yu, K Zhang, ... arXiv preprint arXiv:1901.05350, 2019 | 66 | 2019 |
| 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) M Abadi, P Barham, J Chen, Z Chen, A Davis, J Dean, M Devin, ... USENIX Association, 265-283, 2016 | 57 | 2016 |
| Deep networks with large output spaces S Vijayanarasimhan, J Shlens, R Monga, J Yagnik arXiv preprint arXiv:1412.7479, 2014 | 56 | 2014 |
| Dynamic control flow in large-scale machine learning Y Yu, M Abadi, P Barham, E Brevdo, M Burrows, A Davis, J Dean, ... Proceedings of the Thirteenth EuroSys Conference, 1-15, 2018 | 40 | 2018 |
| TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning A Agrawal, AN Modi, A Passos, A Lavoie, A Agarwal, A Shankar, ... arXiv preprint arXiv:1903.01855, 2019 | 36 | 2019 |
| Training a model using parameter server shards GS Corrado, K Chen, JA Dean, S Bengio, R Monga, M Devin US Patent 8,768,870, 2014 | 28 | 2014 |
| Revisiting distributed synchronous sgd X Pan, J Chen, R Monga, S Bengio, R Jozefowicz arXiv preprint arXiv:1702.05800, 2017 | 18 | 2017 |
| SysML: The New Frontier of Machine Learning Systems. A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... | 16 | 2019 |
Jeff DeanGoogle Senior Fellow & SVP, Google Research and HealthVerified email at google.com
