| 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 | 1937 | 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 | 1047 | 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 | 787 | 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 | 758 | 2019 |
| Stacked What-Where Auto-encoders J Zhao, M Mathieu, R Goroshin, Y LeCun https://arxiv.org/abs/1506.02351, 2016 | 365 | 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 | 187 | 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 | 187 | 2015 |
| Learning to linearize under uncertainty R Goroshin, MF Mathieu, Y LeCun Advances in neural information processing systems 28, 2015 | 155 | 2015 |
| Saturating auto-encoders R Goroshin, Y LeCun arXiv preprint arXiv:1301.3577, 2013 | 64 | 2013 |
| Unsupervised feature learning from temporal data R Goroshin, J Bruna, J Tompson, D Eigen, Y LeCun arXiv preprint arXiv:1504.02518, 2015 | 46 | 2015 |
| 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 | 44 | 2021 |
| 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 | 39 | 2021 |
| An effective anti-aliasing approach for residual networks C Vasconcelos, H Larochelle, V Dumoulin, NL Roux, R Goroshin arXiv preprint arXiv:2011.10675, 2020 | 31 | 2020 |
| Block-state transformers J Pilault, M Fathi, O Firat, C Pal, PL Bacon, R Goroshin Advances in Neural Information Processing Systems 36, 2024 | 30* | 2024 |
| Approximate solutions to several visibility optimization problems R Goroshin, Q Huynh, HM Zhou Communications in Mathematical Sciences 9 (2), 535-550, 2011 | 24 | 2011 |
| Learned image compression for machine perception F Codevilla, JG Simard, R Goroshin, C Pal arXiv preprint arXiv:2111.02249, 2021 | 21 | 2021 |
| Learning to navigate in complex environments. arXiv 2016 P Mirowski, R Pascanu, F Viola, H Soyer, AJ Ballard, A Banino, M Denil, ... arXiv preprint arXiv:1611.03673, 2016 | 21 | 2016 |
| Bootstap: Bootstrapped training for tracking-any-point C Doersch, P Luc, Y Yang, D Gokay, S Koppula, A Gupta, J Heyward, ... Proceedings of the Asian Conference on Computer Vision, 3257-3274, 2024 | 18 | 2024 |
| 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 | 18 | 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 | 17 | 2016 |
Yann LeCunChief AI Scientist at Facebook & JT Schwarz Professor at the Courant Institute, New York UniversityOverená e-mailová adresa na: cs.nyu.edu