TensorFlow: Large-scale machine learning on heterogeneous systems M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... | 31520* | 2015 |
Generating sentences from a continuous space SR Bowman, L Vilnis, O Vinyals, AM Dai, R Jozefowicz, S Bengio arXiv preprint arXiv:1511.06349, 2015 | 2807 | 2015 |
An empirical exploration of recurrent network architectures R Jozefowicz, W Zaremba, I Sutskever | 2443 | 2015 |
Improved variational inference with inverse autoregressive flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling Advances in neural information processing systems 29, 2016 | 2114 | 2016 |
Dota 2 with large scale deep reinforcement learning C Berner, G Brockman, B Chan, V Cheung, P Dębiak, C Dennison, ... arXiv preprint arXiv:1912.06680, 2019 | 1906 | 2019 |
Learning dexterous in-hand manipulation OAIM Andrychowicz, B Baker, M Chociej, R Jozefowicz, B McGrew, ... The International Journal of Robotics Research 39 (1), 3-20, 2020 | 1765 | 2020 |
Exploring the limits of language modeling R Jozefowicz, O Vinyals, M Schuster, N Shazeer, Y Wu arXiv preprint arXiv:1602.02410, 2016 | 1422 | 2016 |
Revisiting distributed synchronous SGD J Chen, X Pan, R Monga, S Bengio, R Jozefowicz arXiv preprint arXiv:1604.00981, 2016 | 934 | 2016 |
Inferring single-trial neural population dynamics using sequential auto-encoders C Pandarinath, DJ O’Shea, J Collins, R Jozefowicz, SD Stavisky, JC Kao, ... Nature methods 15 (10), 805-815, 2018 | 634 | 2018 |
Learning to generate reviews and discovering sentiment A Radford, R Jozefowicz, I Sutskever arXiv preprint arXiv:1704.01444, 2017 | 574 | 2017 |
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 52, 2015 | 521 | 2015 |
Tensorflow: Large-scale machine learning on heterogeneous distributed systems. CoRR abs/1603.04467 (2016) M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467, 2016 | 327 | 2016 |
TensorFlow: Large-scale machine learning on heterogeneous systems (2015), software available from tensorflow. org M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... | 291 | 2019 |
TensorFlow: Large-scale machine learning on heterogeneous systems. arXiv 2015 M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467, 2016 | 155 | 2016 |
Dota 2 with large scale deep reinforcement learning CB OpenAI, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ... arXiv preprint arXiv:1912.06680 2, 2019 | 115 | 2019 |
Lfads-latent factor analysis via dynamical systems D Sussillo, R Jozefowicz, LF Abbott, C Pandarinath arXiv preprint arXiv:1608.06315, 2016 | 91 | 2016 |
TensorFlow M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... Large-scale machine learning on heterogeneous systems 11, 2015 | 81 | 2015 |
& Zheng M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... X.: Tensorflow: Large-scale machine learning on heterogeneous distributed …, 2016 | 76 | 2016 |
Towards principled unsupervised learning I Sutskever, R Jozefowicz, K Gregor, D Rezende, T Lillicrap, O Vinyals arXiv preprint arXiv:1511.06440, 2015 | 70 | 2015 |
TensorFlow: Large-scale machine learning on heterogeneous systems, software available from tensorflow. org M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... URL http://tensorflow. org, 2015 | 52 | 2015 |