R. Fergus

发表

Visualizing and Understanding Convolutional Networks

Rob Fergus, Matthew D. Zeiler, R. Fergus, 2013, ECCV.

Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition

Antonio Torralba, William T. Freeman, Rob Fergus, 2022 .

Tiny images

R. Fergus, W. Freeman, 2007 .

Intriguing properties of neural networks pdf

Joan Bruna, Dumitru Erhan, Wojciech Zaremba, 2013, ICLR.

Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play pdf

Ilya Kostrikov, Rob Fergus, Arthur Szlam, 2017, ICLR.

Dark flash photography

R. Fergus, Dilip Krishnan, 2009, ACM Trans. Graph..

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Pietro Perona, Fei-Fei Li, Rob Fergus, 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

Regularization of Neural Networks using DropConnect

Yann LeCun, Li Wan, Rob Fergus, 2013, ICML.

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Pietro Perona, Fei-Fei Li, Rob Fergus, 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

Learning object categories from Google's image search

Pietro Perona, Fei-Fei Li, Andrew Zisserman, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

Joan Bruna, Yann LeCun, Wojciech Zaremba, 2014, NIPS.

One-shot learning of object categories

Pietro Perona, Fei-Fei Li, Rob Fergus, 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Bayesian approach to unsupervised one-shot learning of object categories

Pietro Perona, Fei-Fei Li, Rob Fergus, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

Training Convolutional Networks with Noisy Labels

Joan Bruna, Rob Fergus, Manohar Paluri, 2014, ICLR 2014.

Visualizing and Understanding Convolutional Neural Networks

Rob Fergus, Matthew D. Zeiler, R. Fergus, 2013 .

Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition

Pietro Perona, Andrew Zisserman, Rob Fergus, 2007, International Journal of Computer Vision.

Object class recognition by unsupervised scale-invariant learning

Pietro Perona, Andrew Zisserman, Rob Fergus, 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

Adaptive deconvolutional networks for mid and high level feature learning

Graham W. Taylor, Rob Fergus, Matthew D. Zeiler, 2011, 2011 International Conference on Computer Vision.

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

Myle Ott, R. Fergus, C. L. Zitnick, 2019, Proceedings of the National Academy of Sciences.

Pose-Sensitive Embedding by Nonlinear NCA Regression

Christoph Bregler, Graham W. Taylor, Rob Fergus, 2010, NIPS.

Indoor Segmentation and Support Inference from RGBD Images

Derek Hoiem, Pushmeet Kohli, Nathan Silberman, 2012, ECCV.

Small codes and large image databases for recognition

Antonio Torralba, Rob Fergus, Yair Weiss, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

End-To-End Memory Networks

Jason Weston, Rob Fergus, Arthur Szlam, 2015, NIPS.

A sparse object category model for efficient learning and exhaustive recognition

Pietro Perona, Andrew Zisserman, Rob Fergus, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Understanding Deep Architectures using a Recursive Convolutional Network pdf

Yann LeCun, Rob Fergus, David Eigen, 2013, ICLR.

Convolutional Learning of Spatio-temporal Features

Yann LeCun, Christoph Bregler, Graham W. Taylor, 2010, ECCV.

Learning Physical Intuition of Block Towers by Example

Rob Fergus, Sam Gross, Adam Lerer, 2016, ICML.

A Visual Category Filter for Google Images

Pietro Perona, Andrew Zisserman, Rob Fergus, 2004, ECCV.

Random Lens Imaging

R. Fergus, A. Torralba, W. Freeman, 2006 .

Deconvolutional networks

Graham W. Taylor, Rob Fergus, Dilip Krishnan, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

A Sparse Object Category Model for Efficient Learning and Complete Recognition

Pietro Perona, Andrew Zisserman, Rob Fergus, 2006, Toward Category-Level Object Recognition.

Learning by Asking Questions

Martial Hebert, Abhinav Gupta, Laurens van der Maaten, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

Rob Fergus, David Eigen, R. Fergus, 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines

Leonid Sigal, Graham W. Taylor, Rob Fergus, 2011, NIPS.

Indoor scene segmentation using a structured light sensor

Nathan Silberman, Rob Fergus, R. Fergus, 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

A Bayesian approach to unsupervised one-shot learning of object categories

Pietro Perona, Li Fei-Fei, Rob Fergus, 2003, ICCV 2003.

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

Rob Fergus, Soumith Chintala, Arthur Szlam, 2015, NIPS.

Learning invariant features through topographic filter maps

Marc'Aurelio Ranzato, Yann LeCun, Koray Kavukcuoglu, 2009, CVPR.

Learning Simple Algorithms from Examples

Wojciech Zaremba, Tomas Mikolov, Armand Joulin, 2015, ICML.

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

Rob Fergus, David Eigen, Christian Puhrsch, 2014, NIPS.

Semi-Supervised Learning in Gigantic Image Collections

Antonio Torralba, Rob Fergus, Yair Weiss, 2009, NIPS.

Learning Image Decompositions with Hierarchical Sparse Coding

Matthew D. Zeiler, R. Fergus, 2010 .

Stochastic Video Generation with a Learned Prior

Rob Fergus, Emily L. Denton, R. Fergus, 2018, ICML.

Learning Spatiotemporal Features with 3D Convolutional Networks

Lorenzo Torresani, Du Tran, Rob Fergus, 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

C3D: Generic Features for Video Analysis pdf

Lorenzo Torresani, Du Tran, Rob Fergus, 2014, ArXiv.

MazeBase: A Sandbox for Learning from Games pdf

Rob Fergus, Soumith Chintala, Arthur Szlam, 2015, ArXiv.

Image and depth from a conventional camera with a coded aperture

Frédo Durand, William T. Freeman, Rob Fergus, 2007, ACM Trans. Graph..

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

Rob Fergus, David Eigen, Dilip Krishnan, 2013, 2013 IEEE International Conference on Computer Vision.

Weakly Supervised Memory Networks pdf

Jason Weston, Rob Fergus, Arthur Szlam, 2015, ArXiv.

Learning invariance through imitation

Christoph Bregler, Graham W. Taylor, Rob Fergus, 2011, CVPR 2011.

Removing camera shake from a single photograph

William T. Freeman, Aaron Hertzmann, Rob Fergus, 2006, SIGGRAPH 2006.

Visual object category recognition

Rob Fergus, R. Fergus, 2005 .

Image and depth from a conventional camera with a coded aperture

Frédo Durand, William T. Freeman, Rob Fergus, 2007, SIGGRAPH 2007.

Learning to Discover Efficient Mathematical Identities

Wojciech Zaremba, Rob Fergus, Karol Kurach, 2014, NIPS.

Improving Image Classification with Location Context

Fei-Fei Li, Rob Fergus, Manohar Paluri, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Removing camera shake from a single photograph

William T. Freeman, Aaron Hertzmann, Rob Fergus, 2006, ACM Trans. Graph..

Deep Neural Networks Predict Category Typicality Ratings for Images

Wojciech Zaremba, Rob Fergus, Todd M. Gureckis, 2015, CogSci.

Energy-based models for atomic-resolution protein conformations pdf

Rob Fergus, Yilun Du, Jerry Ma, 2020, ICLR.

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

Rob Fergus, Arthur Szlam, Emily L. Denton, 2018, ICML.

A Hybrid Neural Network-Latent Topic Model

Long Zhu, Li Wan, Rob Fergus, 2012, AISTATS.

Deep End2End Voxel2Voxel Prediction

Lorenzo Torresani, Du Tran, Rob Fergus, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Case for Automated Detection of Diabetic Retinopathy

Lakshminarayanan Subramanian, Nathan Silberman, Rob Fergus, 2010, AAAI Spring Symposium: Artificial Intelligence for Development.

Learning Binary Hash Codes for Large-Scale Image Search

Kristen Grauman, Rob Fergus, R. Fergus, 2013, Machine Learning for Computer Vision.

Beyond frontal faces: Improving Person Recognition using multiple cues

Ning Zhang, Rob Fergus, Yaniv Taigman, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Semantic Label Sharing for Learning with Many Categories

Antonio Torralba, Rob Fergus, Yair Weiss, 2010, ECCV.

Low-level Image Priors and Laplacian Preconditioners for Applications in Computer Graphics and Computational Photography

Rob Fergus, Dilip Krishnan, R. Fergus, 2013 .

Deconvolutional Networks for Feature Learning

Graham W. Taylor, Rob Fergus, Dilip Krishnan, 2010 .

Blind Deconvolution with Re-weighted Sparsity Promotion pdf

Joan Bruna, Rob Fergus, Dilip Krishnan, 2013, ArXiv.

Deep Poselets for Human Detection pdf

Fei Yang, Rob Fergus, Lubomir D. Bourdev, 2014, ArXiv.

Blind deconvolution using a normalized sparsity measure

Rob Fergus, Dilip Krishnan, Terence Tay, 2011, CVPR 2011.

Automatic Data Augmentation for Generalization in Reinforcement Learning

R. Fergus, Ilya Kostrikov, Denis Yarats, 2021, Neural Information Processing Systems.

IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding

Emmanuel Dupoux, Rob Fergus, Adam Lerer, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning invariant features through topographic filter maps

R. Fergus, K. Kavukcuoglu, Yann LeCun, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Deep End2End Voxel2Voxel Prediction

Lorenzo Torresani, Du Tran, Rob Fergus, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Semi-supervised learning and recognition of object classes

R. Fergus, P. Perona, Andrew Zisserman, 2004 .

Differentially private Bayesian learning on distributed data

Mikko A. Heikkilä, R. Fergus, I. Guyon, 2017, NIPS.

Balancing information exposure in social networks

Venkata Rama Kiran Garimella, R. Fergus, S. Vishwanathan, 2017, NIPS.

International Journal of Innovative Technology and Exploring Engineering (IJITEE)

Matthew D. Zeiler, R. Fergus, F. Li, 2019 .

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

R. Fergus, Pierre Sermanet, Michaël Mathieu, 2013, ICLR.

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

R. Fergus, Ilya Kostrikov, Denis Yarats, 2020, ICLR.

Tiny images

R. Fergus, W. Freeman, 2007 .

Understanding the Asymptotic Performance of Model-Based RL Methods

Rob Fergus, William F. Whitney, R. Fergus, 2018 .

Object and scene recognition in tiny images

Antonio Torralba, William T. Freeman, Rob Fergus, 2010 .

Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies

Abhinav Gupta, Rob Fergus, Arthur Szlam, 2019, ICLR.

Learning binary projections for large scale image search

Kristen Grauman, Rob Fergus, R. Fergus, 2011 .

Differentiable pooling for hierarchical feature learning: arXiv 1207.0151v1

Rob Fergus, Matthew D. Zeiler, R. Fergus, 2012 .

UvA-DARE (Digital Academic Repository) Causal Effect Inference with Deep Latent-Variable Models

R. Fergus, I. Guyon, Samy Bengio, 2017 .

Teacher Guided Training: An Efficient Framework for Knowledge Transfer

R. Fergus, M. Zaheer, A. Rawat, 2022, ICLR.

Bayesian Compression for Deep Learning

R. Fergus, I. Guyon, Samy Bengio, 2017 .

Perturbing BatchNorm and Only BatchNorm Benefits Sharpness-Aware Minimization

R. Fergus, Samy Bengio, K. Grauman, 2022 .

Learning to Navigate Wikipedia by Taking Random Walks

R. Fergus, Will Grathwohl, Arun Ahuja, 2022, NeurIPS.

Differentiable Pooling for Hierarchical Feature Learning pdf

Rob Fergus, Matthew D. Zeiler, R. Fergus, 2012, ArXiv.

Spectral Hashing

Antonio Torralba, Rob Fergus, Yair Weiss, 2008, NIPS.

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval

R. Fergus, M. Zaheer, Wittawat Jitkrittum, 2023, ArXiv.

Object Recognition by Scene Alignment

Antonio Torralba, William T. Freeman, Ce Liu, 2007, NIPS.

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks pdf

Xiang Zhang, Yann LeCun, Pierre Sermanet, 2013, ICLR.

Instance Segmentation of Indoor Scenes Using a Coverage Loss

Nathan Silberman, Rob Fergus, David Sontag, 2014, ECCV.

Learning Multiagent Communication with Backpropagation

Rob Fergus, Arthur Szlam, Sainbayar Sukhbaatar, 2016, NIPS.

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks pdf

Rob Fergus, Sam Gross, Emily L. Denton, 2016, ArXiv.

Multidimensional Spectral Hashing

Antonio Torralba, Rob Fergus, Yair Weiss, 2012, ECCV.

Fast Image Deconvolution using Hyper-Laplacian Priors

Rob Fergus, Dilip Krishnan, R. Fergus, 2009, NIPS.

B IOLOGICAL S TRUCTURE AND F UNCTION E MERGE FROM S CALING U NSUPERVISED L EARNING TO 250 M ILLION P ROTEIN S EQUENCES

Myle Ott, R. Fergus, C. L. Zitnick, 2019 .

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Joelle Pineau, Ilya Kostrikov, Rob Fergus, 2019, AAAI.

Blind Deconvolution with Non-local Sparsity Reweighting

Joan Bruna, Rob Fergus, Dilip Krishnan, 2013, 1311.4029.

Reinforcement Learning with Prototypical Representations

Alessandro Lazaric, Rob Fergus, Denis Yarats, 2021, ICML.

Offline Reinforcement Learning with Fisher Divergence Critic Regularization

Ilya Kostrikov, Jonathan Tompson, Rob Fergus, 2021, ICML.

Task Episode ( supervision from external reward ) Alice ’ s turn Bob ’ s turn

R. Fergus, Sainbayar Sukhbaatar, Ilya Kostrikov, 2017 .

Finding Generalizable Evidence by Learning to Convince Q&A Models pdf

Jason Weston, Kyunghyun Cho, Rob Fergus, 2019, EMNLP.

Stochastic Pooling for Regularization of Deep Convolutional Neural Networks pdf

Rob Fergus, Matthew D. Zeiler, R. Fergus, 2013, ICLR.

Collaborating with language models for embodied reasoning

R. Fergus, Arun Ahuja, Felix Hill, 2023, ArXiv.

Decoupling Value and Policy for Generalization in Reinforcement Learning pdf

Rob Fergus, Roberta Raileanu, R. Fergus, 2021, ICML.

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning pdf

Ilya Kostrikov, Rob Fergus, Denis Yarats, 2020, ArXiv.

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

R. Fergus, Will Grathwohl, J. Tenenbaum, 2023, ArXiv.

UvA-DARE (Digital Academic Repository) Causal Effect Inference with Deep Latent-Variable Models Causal Effect Inference with Deep Latent-Variable Models

R. Fergus, Samy Bengio, R. Garnett, 2017 .

Accelerating exploration and representation learning with offline pre-training

R. Fergus, Doina Precup, Jake Bruce, 2023, ArXiv.

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning pdf

Alessandro Lazaric, Rob Fergus, Denis Yarats, 2021, ICLR.

Nonparametric image parsing using adaptive neighbor sets

Rob Fergus, David Eigen, R. Fergus, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Imitation by Predicting Observations

Andrew Jaegle, Rob Fergus, Arun Ahuja, 2021, ICML.

Learning to Discover Efficient Mathematical Identities

R. Fergus, Wojciech Zaremba, Karol Kurach, 2014, NIPS.

Learning from Noisy Labels with Deep Neural Networks pdf

Rob Fergus, Sainbayar Sukhbaatar, R. Fergus, 2014, ICLR.

Simple Baseline for Visual Question Answering pdf

Yuandong Tian, Bolei Zhou, Rob Fergus, 2015, ArXiv.

Distilling Internet-Scale Vision-Language Models into Embodied Agents

R. Fergus, Arun Ahuja, T. Sumers, 2023, arXiv.org.

IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning pdf

Emmanuel Dupoux, Rob Fergus, Mathieu Bernard, 2018, ArXiv.

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning pdf

Rob Fergus, Arthur Szlam, Sainbayar Sukhbaatar, 2018, ArXiv.

Distilling Internet-Scale Vision-Language Models into Embodied Agents

R. Fergus, Arun Ahuja, Ishita Dasgupta, 2023, ICML.

Composable Planning with Attributes pdf

Rob Fergus, Arthur Szlam, Sainbayar Sukhbaatar, 2018, ICML.

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

Li Wan, Rob Fergus, David Eigen, 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

R. Fergus, Will Grathwohl, J. Tenenbaum, 2023, ICML.

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval

R. Fergus, M. Zaheer, Wittawat Jitkrittum, 2023, ArXiv.

Learning Object Categories From Internet Image Searches

Pietro Perona, Fei-Fei Li, Andrew Zisserman, 2010, Proceedings of the IEEE.

Learning Object Categories From Internet Image Searches This paper shows how the results returned by an image search engine can be used to construct models from Internet images and use them for object recognition.

Pietro Perona, Andrew Zisserman, Rob Fergus, 2010 .

Learning from Noisy Labels with Deep Neural Networks

R. Fergus, Sainbayar Sukhbaatar, 2014, ICLR.

Fast Adaptation via Policy-Dynamics Value Functions

Rob Fergus, Arthur Szlam, Roberta Raileanu, 2020, ICML.

User Conditional Hashtag Prediction for Images

Emily Denton, Jason Weston, Rob Fergus, 2015, KDD.

Web scale photo hash clustering on a single machine

Fei Yang, Rob Fergus, Lubomir D. Bourdev, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

B IOLOGICAL S TRUCTURE AND F UNCTION E MERGE FROM S CALING U NSUPERVISED L EARNING TO 250 M ILLION P ROTEIN S EQUENCES

Myle Ott, R. Fergus, C. L. Zitnick, 2019 .

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

R. Fergus, D. Eigen, Christian Puhrsch, 2014, NIPS.

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

R. Fergus, D. Eigen, 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

Empirically Verifying Hypotheses Using Reinforcement Learning pdf

Abhinav Gupta, Rob Fergus, Arthur Szlam, 2020, ArXiv.

A Bayesian network framework for relational shape matching

Alan L. Yuille, James M. Coughlan, Anand Rangarajan, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.