Fergus, Rob

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 15,839

Learning Spatiotemporal Features with 3D Convolutional Networks

Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Human Pose and Action RecognitionAdvanced Neural Network Applications

Cited by 9,593

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv

Adversarial Robustness in Machine LearningNeural Networks and Applications

Cited by 8,143

Indoor segmentation and support inference from rgbd images

Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science

3D Shape Modeling and AnalysisAdvanced Neural Network Applications

Cited by 5,760

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

Eigen, D., Puhrsch, C., & Fergus, R. (2014) · arXiv

Advanced Vision and ImagingAdvanced Neural Network Applications

Cited by 3,442

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 3,286

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · Proceedings of the National Academy of Sciences

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 3,280

One-shot learning of object categories

Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 3,029

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsAdvanced Vision and Imaging

Cited by 2,862

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 2,680

Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

Fei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding

Domain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

Cited by 2,359

80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

Torralba, A., Fergus, R., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 1,921

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

Denton, R., Zaremba, W., Bruna, J., LeCun, Y., & Fergus, R. (2014) · arXiv

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 1,414

End-to-end memory networks

Sukhbaatar, S., Szlam, A., Weston, J., & Fergus, R. (2015) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,345

Blind deconvolution using a normalized sparsity measure

Krishnan, D., Tay, T., & Fergus, R. (2011) · CVPR 2011

Advanced Image Processing TechniquesImage and Signal Denoising Methods

Cited by 1,119

Small codes and large image databases for recognition

Torralba, A., Fergus, R., & Weiss, Y. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 727

Visualizing and Understanding Convolutional Networks

Zeiler, M. D., & Fergus, R. (2013) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 522

Restoring an Image Taken through a Window Covered with Dirt or Rain

Eigen, D., Krishnan, D., & Fergus, R. (2013) · 2013 IEEE International Conference on Computer Vision

Image Enhancement TechniquesAdvanced Image Processing Techniques

Cited by 454

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

Kostrikov, I., Yarats, D., & Fergus, R. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 267

Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

Sukhbaatar, S., Lin, Z., Kostrikov, I., Synnaeve, G., Szlam, A., & Fergus, R. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 149

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Yarats, D., Fergus, R., Lazaric, A., & Pinto, L. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 89

End-to-end integration of a Convolutional Network, Deformable Parts Model and non-maximum suppression

Wan, L., Eigen, D., & Fergus, R. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 86

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Tong, S., Brown, E., Wu, P., Woo, S., Middepogu, M., Akula, S. C., Yang, J., Yang, S., Iyer, A., Pan, X., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 68

Finding Generalizable Evidence by Learning to Convince Q&A Models

Perez, E., Karamcheti, S., Fergus, R., Weston, J., Kiela, D., & Cho, K. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 27