Object Class Recognition by Unsupervised Scale-Invariant Learning. - Robert Fergus; Pietro Perona; Andrew Zisserman (CVPR (2), 2003)

A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories. - Li Fei-Fei 001; Robert Fergus; Pietro Perona (ICCV, 2003)

A Visual Category Filter for Google Images. - Robert Fergus; Pietro Perona; Andrew Zisserman (ECCV (1), 2004)

Sampling Methods for Unsupervised Learning. - Robert Fergus; Andrew Zisserman; Pietro Perona (NIPS, 2004)

A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition. - Robert Fergus; Pietro Perona; Andrew Zisserman (CVPR (1), 2005)

Learning Object Categories from Google's Image Search. - Robert Fergus; Li Fei-Fei 001; Pietro Perona; Andrew Zisserman (ICCV, 2005)

Visual object category recognition. - Robert Fergus (2005)

A Sparse Object Category Model for Efficient Learning and Complete Recognition. - Robert Fergus; Pietro Perona; Andrew Zisserman (Toward Category-Level Object Recognition, 2006)

One-Shot Learning of Object Categories. - Li Fei-Fei 001; Robert Fergus; Pietro Perona (IEEE Trans. Pattern Anal. Mach. Intell., 2006)

Removing camera shake from a single photograph. - Robert Fergus; Barun Singh; Aaron Hertzmann; Sam T. Roweis; William T. Freeman (ACM Trans. Graph., 2006)

Object Recognition by Scene Alignment. - Bryan C. Russell; Antonio Torralba 001; Ce Liu 001; Robert Fergus; William T. Freeman (NIPS, 2007)

Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. - Li Fei-Fei 001; Robert Fergus; Pietro Perona (Comput. Vis. Image Underst., 2007)

Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition. - Robert Fergus; Pietro Perona; Andrew Zisserman (Int. J. Comput. Vis., 2007)

Image and depth from a conventional camera with a coded aperture. - Anat Levin; Robert Fergus; Frédo Durand; William T. Freeman (ACM Trans. Graph., 2007)

Small codes and large image databases for recognition. - Antonio Torralba 001; Robert Fergus; Yair Weiss (CVPR, 2008)

Spectral Hashing. - Yair Weiss; Antonio Torralba 001; Robert Fergus (NIPS, 2008)

80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition. - Antonio Torralba 001; Robert Fergus; William T. Freeman (IEEE Trans. Pattern Anal. Mach. Intell., 2008)

Learning invariant features through topographic filter maps. - Koray Kavukcuoglu; Marc'Aurelio Ranzato; Rob Fergus; Yann LeCun (CVPR, 2009)

Semi-Supervised Learning in Gigantic Image Collections. - Rob Fergus; Yair Weiss; Antonio Torralba 001 (NIPS, 2009)

Fast Image Deconvolution using Hyper-Laplacian Priors. - Dilip Krishnan; Rob Fergus (NIPS, 2009)

Dark flash photography. - Dilip Krishnan; Rob Fergus (ACM Trans. Graph., 2009)

Case for Automated Detection of Diabetic Retinopathy. - Nathan Silberman; Kristy Ahrlich; Rob Fergus; Lakshminarayanan Subramanian (AAAI Spring Symposium: Artificial Intelligence for Development, 2010)

Deconvolutional networks. - Matthew D. Zeiler; Dilip Krishnan; Graham W. Taylor; Robert Fergus (CVPR, 2010)

Semantic Label Sharing for Learning with Many Categories. - Robert Fergus; Hector Bernal; Yair Weiss; Antonio Torralba 001 (ECCV (1), 2010)

Convolutional Learning of Spatio-temporal Features. - Graham W. Taylor; Rob Fergus; Yann LeCun; Christoph Bregler (ECCV (6), 2010)

Pose-Sensitive Embedding by Nonlinear NCA Regression. - Graham W. Taylor; Rob Fergus; George Williams; Ian Spiro; Christoph Bregler (NIPS, 2010)

Learning Object Categories From Internet Image Searches. - Robert Fergus; Li Fei-Fei 001; Pietro Perona; Andrew Zisserman (Proc. IEEE, 2010)

Blind deconvolution using a normalized sparsity measure. - Dilip Krishnan; Terence Tay; Rob Fergus (CVPR, 2011)

Learning invariance through imitation. - Graham W. Taylor; Ian Spiro; Christoph Bregler; Rob Fergus (CVPR, 2011)

Adaptive deconvolutional networks for mid and high level feature learning. - Matthew D. Zeiler; Graham W. Taylor; Rob Fergus (ICCV, 2011)

Indoor scene segmentation using a structured light sensor. - Nathan Silberman; Rob Fergus (ICCV Workshops, 2011)

Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines. - Matthew D. Zeiler; Graham W. Taylor; Leonid Sigal; Iain A. Matthews; Rob Fergus (NIPS, 2011)

Nonparametric image parsing using adaptive neighbor sets. - David Eigen; Rob Fergus (CVPR, 2012)

Indoor Segmentation and Support Inference from RGBD Images. - Nathan Silberman; Derek Hoiem; Pushmeet Kohli; Rob Fergus (ECCV (5), 2012)

Multidimensional Spectral Hashing. - Yair Weiss; Rob Fergus; Antonio Torralba 001 (ECCV (5), 2012)

Differentiable Pooling for Hierarchical Feature Learning - Matthew D. Zeiler; Rob Fergus (CoRR, 2012)

A Hybrid Neural Network-Latent Topic Model. - Li Wan 002; Leo Zhu; Rob Fergus (AISTATS, 2012)

Restoring an Image Taken through a Window Covered with Dirt or Rain. - David Eigen; Dilip Krishnan; Rob Fergus (ICCV, 2013)

Regularization of Neural Networks using DropConnect. - Li Wan 002; Matthew D. Zeiler; Sixin Zhang; Yann LeCun; Rob Fergus (ICML (3), 2013)

Blind Deconvolution with Re-weighted Sparsity Promotion. - Dilip Krishnan; Joan Bruna; Rob Fergus (CoRR, 2013)

Stochastic Pooling for Regularization of Deep Convolutional Neural Networks - Matthew D. Zeiler; Rob Fergus (ICLR, 2013)

Learning Binary Hash Codes for Large-Scale Image Search. - Kristen Grauman; Rob Fergus (Machine Learning for Computer Vision, 2013)

Instance Segmentation of Indoor Scenes Using a Coverage Loss. - Nathan Silberman; David A. Sontag; Rob Fergus (ECCV (1), 2014)

Visualizing and Understanding Convolutional Networks. - Matthew D. Zeiler; Rob Fergus (ECCV (1), 2014)

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. - Emily L. Denton; Wojciech Zaremba; Joan Bruna; Yann LeCun; Rob Fergus (NIPS, 2014)

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. - David Eigen; Christian Puhrsch; Rob Fergus (NIPS, 2014)

Learning to Discover Efficient Mathematical Identities. - Wojciech Zaremba; Karol Kurach; Rob Fergus (NIPS, 2014)

Deep Poselets for Human Detection. - Lubomir D. Bourdev; Fei Yang 001; Rob Fergus (CoRR, 2014)

Understanding Deep Architectures using a Recursive Convolutional Network. - David Eigen; Jason Tyler Rolfe; Rob Fergus; Yann LeCun (ICLR (Workshop Poster), 2014)

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. - Pierre Sermanet; David Eigen; Xiang Zhang 013; Michaël Mathieu; Rob Fergus; Yann LeCun (ICLR, 2014)

Intriguing properties of neural networks. - Christian Szegedy; Wojciech Zaremba; Ilya Sutskever; Joan Bruna; Dumitru Erhan; Ian J. Goodfellow; Rob Fergus (ICLR (Poster), 2014)

C3D: Generic Features for Video Analysis. - Du Tran; Lubomir D. Bourdev; Rob Fergus; Lorenzo Torresani; Manohar Paluri (CoRR, 2014)

Deep Neural Networks Predict Category Typicality Ratings for Images. - Brenden M. Lake; Wojciech Zaremba; Rob Fergus; Todd M. Gureckis (CogSci, 2015)

Web scale photo hash clustering on a single machine. - Yunchao Gong; Marcin Pawlowski 003; Fei Yang 001; Louis Brandy; Lubomir D. Bourdev; Rob Fergus (CVPR, 2015)

End-to-end integration of a Convolutional Network, Deformable Parts Model and non-maximum suppression. - Li Wan 002; David Eigen; Rob Fergus (CVPR, 2015)

Beyond frontal faces: Improving Person Recognition using multiple cues. - Ning Zhang 014; Manohar Paluri; Yaniv Taigman; Rob Fergus; Lubomir D. Bourdev (CVPR, 2015)

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture. - David Eigen; Rob Fergus (ICCV, 2015)

Improving Image Classification with Location Context. - Kevin D. Tang; Manohar Paluri; Li Fei-Fei 001; Robert Fergus; Lubomir D. Bourdev (ICCV, 2015)

Learning Spatiotemporal Features with 3D Convolutional Networks. - Du Tran; Lubomir D. Bourdev; Rob Fergus; Lorenzo Torresani; Manohar Paluri (ICCV, 2015)

User Conditional Hashtag Prediction for Images. - Emily Denton; Jason Weston; Manohar Paluri; Lubomir D. Bourdev; Rob Fergus (KDD, 2015)

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. - Emily L. Denton; Soumith Chintala; Arthur Szlam; Rob Fergus (NIPS, 2015)

End-To-End Memory Networks. - Sainbayar Sukhbaatar; Arthur Szlam; Jason Weston; Rob Fergus (NIPS, 2015)

Learning from Noisy Labels with Deep Neural Networks. - Sainbayar Sukhbaatar; Rob Fergus (ICLR (Workshop), 2015)

MazeBase: A Sandbox for Learning from Games. - Sainbayar Sukhbaatar; Arthur Szlam; Gabriel Synnaeve; Soumith Chintala; Rob Fergus (CoRR, 2015)

Weakly Supervised Memory Networks. - Sainbayar Sukhbaatar; Arthur Szlam; Jason Weston; Rob Fergus (CoRR, 2015)

Simple Baseline for Visual Question Answering. - Bolei Zhou; Yuandong Tian; Sainbayar Sukhbaatar; Arthur Szlam; Rob Fergus (CoRR, 2015)

Deep End2End Voxel2Voxel Prediction. - Du Tran; Lubomir D. Bourdev; Rob Fergus; Lorenzo Torresani; Manohar Paluri (CVPR Workshops, 2016)

Learning Physical Intuition of Block Towers by Example. - Adam Lerer; Sam Gross; Rob Fergus (ICML, 2016)

Learning Simple Algorithms from Examples. - Wojciech Zaremba; Tomás Mikolov; Armand Joulin; Rob Fergus (ICML, 2016)

Learning Multiagent Communication with Backpropagation. - Sainbayar Sukhbaatar; Arthur Szlam; Rob Fergus (NIPS, 2016)

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks. - Emily L. Denton; Sam Gross; Rob Fergus (CoRR, 2016)

Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play. - Sainbayar Sukhbaatar; Ilya Kostrikov; Arthur Szlam; Rob Fergus (CoRR, 2017)

Learning by Asking Questions. - Ishan Misra; Ross B. Girshick; Rob Fergus; Martial Hebert; Abhinav Gupta 001; Laurens van der Maaten (CVPR, 2018)

Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play. - Sainbayar Sukhbaatar; Zeming Lin; Ilya Kostrikov; Gabriel Synnaeve; Arthur Szlam; Rob Fergus (ICLR (Poster), 2018)

Stochastic Video Generation with a Learned Prior. - Emily Denton; Rob Fergus (ICML, 2018)

Modeling Others using Oneself in Multi-Agent Reinforcement Learning. - Roberta Raileanu; Emily Denton; Arthur Szlam; Rob Fergus (ICML, 2018)

Composable Planning with Attributes. - Amy Zhang 001; Sainbayar Sukhbaatar; Adam Lerer; Arthur Szlam; Rob Fergus (ICML, 2018)

IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning. - Ronan Riochet; Mario Ynocente Castro; Mathieu Bernard; Adam Lerer; Rob Fergus; Véronique Izard; Emmanuel Dupoux (CoRR, 2018)

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning. - Sainbayar Sukhbaatar; Emily Denton; Arthur Szlam; Rob Fergus (CoRR, 2018)

Finding Generalizable Evidence by Learning to Convince Q&A Models. - Ethan Perez; Siddharth Karamcheti; Rob Fergus; Jason Weston; Douwe Kiela; Kyunghyun Cho (EMNLP/IJCNLP (1), 2019)

Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies. - Kenneth Marino; Abhinav Gupta 001; Rob Fergus; Arthur Szlam (ICLR (Poster), 2019)

Disentangling Video with Independent Prediction. - William F. Whitney; Rob Fergus (CoRR, 2019)

Energy-based models for atomic-resolution protein conformations. - Yilun Du; Joshua Meier; Jerry Ma; Rob Fergus; Alexander Rives (ICLR, 2020)

Fast Adaptation to New Environments via Policy-Dynamics Value Functions. - Roberta Raileanu; Maxwell Goldstein; Arthur Szlam; Rob Fergus (ICML, 2020)

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning. - Roberta Raileanu; Maxwell Goldstein; Denis Yarats; Ilya Kostrikov; Rob Fergus (CoRR, 2020)

Empirically Verifying Hypotheses Using Reinforcement Learning. - Kenneth Marino; Rob Fergus; Arthur Szlam; Abhinav Gupta 001 (CoRR, 2020)

Fast Adaptation via Policy-Dynamics Value Functions. - Roberta Raileanu; Maxwell Goldstein; Arthur Szlam; Rob Fergus (CoRR, 2020)

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. - Denis Yarats; Amy Zhang 001; Ilya Kostrikov; Brandon Amos; Joelle Pineau; Rob Fergus (AAAI, 2021)

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels. - Denis Yarats; Ilya Kostrikov; Rob Fergus (ICLR, 2021)

Imitation by Predicting Observations. - Andrew Jaegle; Yury Sulsky; Arun Ahuja; Jake Bruce; Rob Fergus; Greg Wayne (ICML, 2021)

Offline Reinforcement Learning with Fisher Divergence Critic Regularization. - Ilya Kostrikov; Rob Fergus; Jonathan Tompson; Ofir Nachum (ICML, 2021)

Decoupling Value and Policy for Generalization in Reinforcement Learning. - Roberta Raileanu; Rob Fergus (ICML, 2021)

Reinforcement Learning with Prototypical Representations. - Denis Yarats; Rob Fergus; Alessandro Lazaric; Lerrel Pinto (ICML, 2021)

Automatic Data Augmentation for Generalization in Reinforcement Learning. - Roberta Raileanu; Maxwell Goldstein; Denis Yarats; Ilya Kostrikov; Rob Fergus (NeurIPS, 2021)

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. - Alexander Rives; Joshua Meier; Tom Sercu; Siddharth Goyal; Zeming Lin; Jason Liu; Demi Guo; Myle Ott; C. Lawrence Zitnick; Jerry Ma; Rob Fergus (Proc. Natl. Acad. Sci. USA, 2021)

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. - Denis Yarats; Rob Fergus; Alessandro Lazaric; Lerrel Pinto (ICLR, 2022)

Learning to Navigate Wikipedia by Taking Random Walks. - Manzil Zaheer; Kenneth Marino; Will Grathwohl; John Schultz; Wendy Shang; Sheila Babayan; Arun Ahuja; Ishita Dasgupta 001; Christine Kaeser-Chen; Rob Fergus (NeurIPS, 2022)

IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding. - Ronan Riochet; Mario Ynocente Castro; Mathieu Bernard; Adam Lerer; Rob Fergus; Véronique Izard; Emmanuel Dupoux (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Learning About Progress From Experts. - Jake Bruce; Ankit Anand; Bogdan Mazoure; Rob Fergus (ICLR, 2023)

Teacher Guided Training: An Efficient Framework for Knowledge Transfer. - Manzil Zaheer; Ankit Singh Rawat; Seungyeon Kim 001; Chong You; Himanshu Jain; Andreas Veit; Rob Fergus; Sanjiv Kumar (ICLR, 2023)

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. - Yilun Du; Conor Durkan; Robin Strudel; Joshua B. Tenenbaum; Sander Dieleman; Rob Fergus; Jascha Sohl-Dickstein; Arnaud Doucet; Will Sussman Grathwohl (ICML, 2023)

Distilling Internet-Scale Vision-Language Models into Embodied Agents. - Theodore R. Sumers; Kenneth Marino; Arun Ahuja; Rob Fergus; Ishita Dasgupta 001 (ICML, 2023)

NetHack is Hard to Hack. - Ulyana Piterbarg; Lerrel Pinto; Rob Fergus (NeurIPS, 2023)

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Sadeep Jayasumana; Veeranjaneyulu Sadhanala; Wittawat Jitkrittum; Aditya Krishna Menon; Rob Fergus; Sanjiv Kumar (CoRR, 2023)

Collaborating with language models for embodied reasoning. - Ishita Dasgupta 001; Christine Kaeser-Chen; Kenneth Marino; Arun Ahuja; Sheila Babayan; Felix Hill; Rob Fergus (CoRR, 2023)

Accelerating exploration and representation learning with offline pre-training. - Bogdan Mazoure; Jake Bruce; Doina Precup; Rob Fergus; Ankit Anand (CoRR, 2023)

Hierarchical reinforcement learning with natural language subgoals. - Arun Ahuja; Kavya Kopparapu; Rob Fergus; Ishita Dasgupta 001 (CoRR, 2023)

diff History for Long-Context Language Agents. - Ulyana Piterbarg; Lerrel Pinto; Rob Fergus (CoRR, 2023)

Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders. - Nishant Yadav; Nicholas Monath; Manzil Zaheer; Rob Fergus; Andrew McCallum (ICLR, 2024)

USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Wittawat Jitkrittum; Veeranjaneyulu Sadhanala; Sadeep Jayasumana; Aditya Krishna Menon; Rob Fergus; Sanjiv Kumar (ICML, 2024)

A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. - Nicholas Monath; Will Sussman Grathwohl; Michael Boratko; Rob Fergus; Andrew McCallum; Manzil Zaheer (ICML, 2024)

diff History for Neural Language Agents. - Ulyana Piterbarg; Lerrel Pinto; Rob Fergus (ICML, 2024)

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs. - Peter Tong; Ellis Brown; Penghao Wu; Sanghyun Woo; Adithya Iyer; Sai Charitha Akula; Shusheng Yang; Jihan Yang; Manoj Middepogu; Ziteng Wang; Xichen Pan; Rob Fergus; et al. (NeurIPS, 2024)

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs. - Shengbang Tong; Ellis Brown; Penghao Wu; Sanghyun Woo; Manoj Middepogu; Sai Charitha Akula; Jihan Yang; Shusheng Yang; Adithya Iyer; Xichen Pan; Austin Wang; Rob Fergus; et al. (CoRR, 2024)

Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction. - Anthony GX-Chen; Kenneth Marino; Rob Fergus (ICLR, 2025)

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games. - Davide Paglieri; Bartlomiej Cupial; Samuel Coward; Ulyana Piterbarg; Maciej Wolczyk; Akbir Khan; Eduardo Pignatelli; Lukasz Kucinski; Lerrel Pinto; Rob Fergus; Jakob Nicolaus Foerster; Jack Parker-Holder; et al. (ICLR, 2025)

Training Language Models on Synthetic Edit Sequences Improves Code Synthesis. - Ulyana Piterbarg; Lerrel Pinto; Rob Fergus (ICLR, 2025)

Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists? - Anthony GX-Chen; Dongyan Lin; Mandana Samiei; Doina Precup; Blake A. Richards; Rob Fergus; Kenneth Marino (CoRR, 2025)