| In-datacenter performance analysis of a tensor processing unit NP Jouppi, C Young, N Patil, D Patterson, G Agrawal, R Bajwa, S Bates, ... Proceedings of the 44th Annual International Symposium on Computer …, 2017 | 1847 | 2017 |
| Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection S Levine, P Pastor, A Krizhevsky, J Ibarz, D Quillen The International Journal of Robotics Research 37 (4-5), 421-436, 2018 | 1199 | 2018 |
| Multi-digit number recognition from street view imagery using deep convolutional neural networks IJ Goodfellow, Y Bulatov, J Ibarz, S Arnoud, V Shet arXiv preprint arXiv:1312.6082, 2013 | 545 | 2013 |
| Using simulation and domain adaptation to improve efficiency of deep robotic grasping K Bousmalis, A Irpan, P Wohlhart, Y Bai, M Kelcey, M Kalakrishnan, ... 2018 IEEE international conference on robotics and automation (ICRA), 4243-4250, 2018 | 279 | 2018 |
| Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation D Kalashnikov, A Irpan, P Pastor, J Ibarz, A Herzog, E Jang, D Quillen, ... arXiv preprint arXiv:1806.10293, 2018 | 206 | 2018 |
| Diversity is all you need: Learning skills without a reward function B Eysenbach, A Gupta, J Ibarz, S Levine arXiv preprint arXiv:1802.06070, 2018 | 191 | 2018 |
| Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks S James, P Wohlhart, M Kalakrishnan, D Kalashnikov, A Irpan, J Ibarz, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 90 | 2019 |
| Attention-based extraction of structured information from street view imagery Z Wojna, AN Gorban, DS Lee, K Murphy, Q Yu, Y Li, J Ibarz 2017 14th IAPR International Conference on Document Analysis and Recognition …, 2017 | 82 | 2017 |
| Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods D Quillen, E Jang, O Nachum, C Finn, J Ibarz, S Levine 2018 IEEE International Conference on Robotics and Automation (ICRA), 6284-6291, 2018 | 77 | 2018 |
| Scalable deep reinforcement learning for vision-based robotic manipulation D Kalashnikov, A Irpan, P Pastor, J Ibarz, A Herzog, E Jang, D Quillen, ... Conference on Robot Learning, 651-673, 2018 | 76 | 2018 |
| Discrete sequential prediction of continuous actions for deep rl L Metz, J Ibarz, N Jaitly, J Davidson arXiv preprint arXiv:1705.05035, 2017 | 45 | 2017 |
| Leave no trace: Learning to reset for safe and autonomous reinforcement learning B Eysenbach, S Gu, J Ibarz, S Levine arXiv preprint arXiv:1711.06782, 2017 | 40 | 2017 |
| End-to-end learning of semantic grasping E Jang, S Vijayanarasimhan, P Pastor, J Ibarz, S Levine arXiv preprint arXiv:1707.01932, 2017 | 40 | 2017 |
| End-to-end interpretation of the french street name signs dataset R Smith, C Gu, DS Lee, H Hu, R Unnikrishnan, J Ibarz, S Arnoud, S Lin European Conference on Computer Vision, 411-426, 2016 | 33 | 2016 |
| Sequence transcription with deep neural networks J Ibarz, Y Bulatov, I Goodfellow US Patent 8,965,112, 2015 | 20 | 2015 |
| Updating geographic data based on a transaction M Zennaro, K man Cheung, J Ibarz, L Yatziv, SC Arnoud US Patent 8,868,522, 2014 | 19 | 2014 |
| Three-dimensional esophageal reconstruction J Ibarz, N Strobel, L Yatziv US Patent 8,538,106, 2013 | 9 | 2013 |
| System and method for view-dependent anatomic surface visualization J Ibarz, L Yatziv, N Strobel US Patent App. 12/875,191, 2011 | 9 | 2011 |
| Off-policy evaluation via off-policy classification A Irpan, K Rao, K Bousmalis, C Harris, J Ibarz, S Levine Advances in Neural Information Processing Systems, 5437-5448, 2019 | 8 | 2019 |
| Large scale business discovery from street level imagery Q Yu, C Szegedy, MC Stumpe, L Yatziv, V Shet, J Ibarz, S Arnoud arXiv preprint arXiv:1512.05430, 2015 | 6 | 2015 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
