| Visualizing and understanding convolutional networks MD Zeiler, R Fergus European conference on computer vision, 818-833, 2014 | 11652 | 2014 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 6455 | 2013 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 6455 | 2013 |
| Learning spatiotemporal features with 3d convolutional networks D Tran, L Bourdev, R Fergus, L Torresani, M Paluri Proceedings of the IEEE international conference on computer vision, 4489-4497, 2015 | 4576 | 2015 |
| Overfeat: Integrated recognition, localization and detection using convolutional networks P Sermanet, D Eigen, X Zhang, M Mathieu, R Fergus, Y LeCun arXiv preprint arXiv:1312.6229, 2013 | 4541 | 2013 |
| Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories L Fei-Fei, R Fergus, P Perona 2004 conference on computer vision and pattern recognition workshop, 178-178, 2004 | 3976 | 2004 |
| Indoor segmentation and support inference from rgbd images N Silberman, D Hoiem, P Kohli, R Fergus European conference on computer vision, 746-760, 2012 | 3093 | 2012 |
| Object class recognition by unsupervised scale-invariant learning R Fergus, P Perona, A Zisserman 2003 IEEE Computer Society Conference on Computer Vision and Pattern …, 2003 | 2922 | 2003 |
| Spectral hashing. Y Weiss, A Torralba, R Fergus Nips 1 (2), 4, 2008 | 2608 | 2008 |
| Regularization of neural networks using dropconnect L Wan, M Zeiler, S Zhang, Y Le Cun, R Fergus International conference on machine learning, 1058-1066, 2013 | 2191 | 2013 |
| Removing camera shake from a single photograph R Fergus, B Singh, A Hertzmann, ST Roweis, WT Freeman ACM SIGGRAPH 2006 Papers, 787-794, 2006 | 2185 | 2006 |
| Depth map prediction from a single image using a multi-scale deep network D Eigen, C Puhrsch, R Fergus arXiv preprint arXiv:1406.2283, 2014 | 2083 | 2014 |
| End-to-end memory networks S Sukhbaatar, A Szlam, J Weston, R Fergus arXiv preprint arXiv:1503.08895, 2015 | 1983 | 2015 |
| One-shot learning of object categories L Fei-Fei, R Fergus, P Perona IEEE transactions on pattern analysis and machine intelligence 28 (4), 594-611, 2006 | 1914 | 2006 |
| Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture D Eigen, R Fergus Proceedings of the IEEE international conference on computer vision, 2650-2658, 2015 | 1855 | 2015 |
| 80 million tiny images: A large data set for nonparametric object and scene recognition A Torralba, R Fergus, WT Freeman IEEE transactions on pattern analysis and machine intelligence 30 (11), 1958 …, 2008 | 1828 | 2008 |
| Deep generative image models using a laplacian pyramid of adversarial networks E Denton, S Chintala, A Szlam, R Fergus arXiv preprint arXiv:1506.05751, 2015 | 1826 | 2015 |
| Image and depth from a conventional camera with a coded aperture A Levin, R Fergus, F Durand, WT Freeman ACM transactions on graphics (TOG) 26 (3), 70-es, 2007 | 1660 | 2007 |
| Deconvolutional networks MD Zeiler, D Krishnan, GW Taylor, R Fergus 2010 IEEE Computer Society Conference on computer vision and pattern …, 2010 | 1391 | 2010 |
| Exploiting linear structure within convolutional networks for efficient evaluation E Denton, W Zaremba, J Bruna, Y LeCun, R Fergus arXiv preprint arXiv:1404.0736, 2014 | 1203 | 2014 |
