| Visualizing and Understanding Convolutional Networks MD Zeiler European conference on computer vision/arXiv 1311, 2014 | 23468 | 2014 |
| Intriguing properties of neural networks C Szegedy arXiv preprint arXiv:1312.6199, 2013 | 17938 | 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 | 10649 | 2015 |
| Overfeat: Integrated Recognition, Localization and Detection Using Convolutional networks P Sermanet arXiv preprint arXiv:1312.6229, 2013 | 7888 | 2013 |
| Indoor segmentation and support inference from rgbd images N Silberman, D Hoiem, P Kohli, R Fergus Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 6680 | 2012 |
| 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 | 5750 | 2004 |
| Depth map prediction from a single image using a multi-scale deep network D Eigen, C Puhrsch, R Fergus Advances in neural information processing systems 27, 2014 | 4817 | 2014 |
| 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 | 3412 | 2013 |
| 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 | 3400 | 2015 |
| Spectral hashing Y Weiss, A Torralba, R Fergus Advances in neural information processing systems 21, 2008 | 3284 | 2008 |
| End-to-end memory networks S Sukhbaatar, J Weston, R Fergus Advances in neural information processing systems 28, 2015 | 3216 | 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 | 3195 | 2006 |
| 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 | 3075 | 2003 |
| Deep generative image models using a laplacian pyramid of adversarial networks EL Denton, S Chintala, R Fergus Advances in neural information processing systems 28, 2015 | 3047 | 2015 |
| Removing camera shake from a single photograph R Fergus, B Singh, A Hertzmann, ST Roweis, WT Freeman Acm Siggraph 2006 Papers, 787-794, 2006 | 2747 | 2006 |
| 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 | 2475 | 2008 |
| Deconvolutional networks MD Zeiler, D Krishnan, GW Taylor, R Fergus 2010 IEEE Computer Society Conference on computer vision and pattern …, 2010 | 2359 | 2010 |
| Exploiting linear structure within convolutional networks for efficient evaluation EL Denton, W Zaremba, J Bruna, Y LeCun, R Fergus Advances in neural information processing systems 27, 2014 | 2097 | 2014 |
| Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences A Rives, J Meier, T Sercu, S Goyal, Z Lin, J Liu, D Guo, M Ott, CL Zitnick, ... Proceedings of the National Academy of Sciences 118 (15), e2016239118, 2021 | 2081 | 2021 |
| 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 | 2025 | 2007 |
