| Efficient object localization using convolutional networks J Tompson, R Goroshin, A Jain, Y LeCun, C Bregler Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1522 | 2015 |
| Learning to navigate in complex environments P Mirowski, R Pascanu, F Viola, H Soyer, AJ Ballard, A Banino, M Denil, ... arXiv preprint arXiv:1611.03673, 2016 | 839 | 2016 |
| Vector-based navigation using grid-like representations in artificial agents A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ... Nature 557 (7705), 429-433, 2018 | 574 | 2018 |
| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... arXiv preprint arXiv:1903.03096, 2019 | 485 | 2019 |
| Stacked What-Where Auto-encoders J Zhao, M Mathieu, R Goroshin, Y LeCun https://arxiv.org/abs/1506.02351, 2016 | 353 | 2016 |
| Unsupervised learning of spatiotemporally coherent metrics R Goroshin, J Bruna, J Tompson, D Eigen, Y LeCun Proceedings of the IEEE international conference on computer vision, 4086-4093, 2015 | 164 | 2015 |
| Unsupervised learning of spatiotemporally coherent metrics R Goroshin, J Bruna, J Tompson, D Eigen, Y LeCun Proceedings of the IEEE international conference on computer vision, 4086-4093, 2015 | 164 | 2015 |
| Learning to linearize under uncertainty R Goroshin, MF Mathieu, Y LeCun Advances in neural information processing systems 28, 2015 | 135 | 2015 |
| Saturating auto-encoders R Goroshin, Y LeCun arXiv preprint arXiv:1301.3577, 2013 | 57 | 2013 |
| Unsupervised feature learning from temporal data R Goroshin, J Bruna, J Tompson, D Eigen, Y LeCun arXiv preprint arXiv:1504.02518, 2015 | 48 | 2015 |
| An effective anti-aliasing approach for residual networks C Vasconcelos, H Larochelle, V Dumoulin, NL Roux, R Goroshin arXiv preprint arXiv:2011.10675, 2020 | 23 | 2020 |
| Approximate solutions to several visibility optimization problems R Goroshin, Q Huynh, HM Zhou Communications in Mathematical Sciences 9 (2), 535-550, 2011 | 22 | 2011 |
| Comparing transfer and meta learning approaches on a unified few-shot classification benchmark V Dumoulin, N Houlsby, U Evci, X Zhai, R Goroshin, S Gelly, H Larochelle arXiv preprint arXiv:2104.02638, 2021 | 21 | 2021 |
| Impact of aliasing on generalization in deep convolutional networks C Vasconcelos, H Larochelle, V Dumoulin, R Romijnders, N Le Roux, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 20 | 2021 |
| Learning to navigate in complex environments. arXiv P Mirowski, R Pascanu, F Viola, H Soyer, AJ Ballard, A Banino, M Denil, ... arXiv preprint arXiv:1611.03673, 2016 | 10 | 2016 |
| Automated cable tracking in sonar imagery JC Isaacs, R Goroshin OCEANS 2010 MTS/IEEE SEATTLE, 1-7, 2010 | 8 | 2010 |
| A unified few-shot classification benchmark to compare transfer and meta learning approaches V Dumoulin, N Houlsby, U Evci, X Zhai, R Goroshin, S Gelly, H Larochelle Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 6 | 2021 |
| Learned image compression for machine perception F Codevilla, JG Simard, R Goroshin, C Pal arXiv preprint arXiv:2111.02249, 2021 | 5 | 2021 |
| Code for" Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, K Xu, R Goroshin, ... | 4 | 2019 |
| Efficient object localization using convolutional networks. CoRR abs/1411.4280 (2014) J Tompson, R Goroshin, A Jain, Y LeCun, C Bregler arXiv preprint arXiv:1411.4280, 2014 | 4 | 2014 |
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
