World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
80
Citations
119690
World Ranking
1045
National Ranking
557

Research.com Recognitions

  • 2011 - Fellow of Alfred P. Sloan Foundation

Overview

Rob Fergus is affiliated with New York University in the United States and has a main field of study in Computer Science with a focus on Artificial Intelligence, which accounts for the majority of their research output. Their work extends into subfields such as Computer Vision and Pattern Recognition, Molecular Biology, Statistical and Nonlinear Physics, and Computational Theory and Mathematics.

Their research spans various specialized topics, including Reinforcement Learning in Robotics, Domain Adaptation and Few-Shot Learning, Topic Modeling, Natural Language Processing Techniques, Evolutionary Algorithms and Applications, Advanced Image and Video Retrieval Techniques, and Explainable Artificial Intelligence (XAI).

Frequent collaborators in their research include Denis Yarats, Lerrel Pinto, Manzil Zaheer, Kenneth Marino, and Ilya Kostrikov. These coauthors have contributed extensively across multiple publications, reflecting ongoing collaborative efforts in related areas.

Rob Fergus has contributed to numerous scholarly venues. Their publications frequently appear in arXiv (Cornell University), which hosts 34 of their works. Other publication venues include Proceedings of the National Academy of Sciences, Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Proceedings of the AAAI Conference on Artificial Intelligence, and IEEE Transactions on Pattern Analysis and Machine Intelligence.

Recent papers authored or coauthored by Rob Fergus include:

  • Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2021, Proceedings of the National Academy of Sciences)
  • Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper) (2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl))
  • Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels (2020, arXiv (Cornell University))
  • Improving Sample Efficiency in Model-Free Reinforcement Learning from Images (2021, Proceedings of the AAAI Conference on Artificial Intelligence)

Rob Fergus was recognized as a Fellow of the Alfred P. Sloan Foundation in 2011.

Best Publications

  • Visualizing and Understanding Convolutional Networks

    Matthew D. Zeiler;Rob Fergus

  • Intriguing properties of neural networks

    Christian Szegedy;Wojciech Zaremba;Ilya Sutskever;Joan Bruna

  • Learning Spatiotemporal Features with 3D Convolutional Networks

    Du Tran;Du Tran;Lubomir Bourdev;Rob Fergus;Lorenzo Torresani

  • Indoor segmentation and support inference from RGBD images

    Nathan Silberman;Derek Hoiem;Pushmeet Kohli;Rob Fergus

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

    Pierre Sermanet;David Eigen;Xiang Zhang;Michael Mathieu

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

    Li Fei-Fei;R. Fergus;P. Perona

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

    Li Fei-Fei;Rob Fergus;Pietro Perona

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

    David Eigen;Christian Puhrsch;Rob Fergus

  • One-shot learning of object categories

    Li Fei-Fei;R. Fergus;P. Perona

  • Object class recognition by unsupervised scale-invariant learning

    R. Fergus;P. Perona;A. Zisserman

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

    David Eigen;Rob Fergus

  • Spectral Hashing

    Yair Weiss;Antonio Torralba;Rob Fergus

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

    Alexander Rives;Alexander Rives;Joshua Meier;Tom Sercu;Siddharth Goyal

  • Regularization of Neural Networks using DropConnect

    Li Wan;Matthew Zeiler;Sixin Zhang;Yann Le Cun

  • Removing camera shake from a single photograph

    Rob Fergus;Barun Singh;Aaron Hertzmann;Sam T. Roweis

  • End-to-end memory networks

    Sainbayar Sukhbaatar;Arthur Szlam;Jason Weston;Rob Fergus

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

    A. Torralba;R. Fergus;W.T. Freeman

  • Deep generative image models using a Laplacian pyramid of adversarial networks

    Emily Denton;Soumith Chintala;Arthur Szlam;Rob Fergus

  • Deconvolutional networks

    Matthew D. Zeiler;Dilip Krishnan;Graham W. Taylor;Rob Fergus

  • Image and depth from a conventional camera with a coded aperture

    Anat Levin;Rob Fergus;Frédo Durand;William T. Freeman

  • Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences

    Alexander Rives;Siddharth Goyal;Joshua Meier;Demi Guo

Frequent Co-Authors

Lubomir Bourdev
Lubomir Bourdev WaveOne, Inc.
Arthur Szlam
Arthur Szlam DeepMind (United Kingdom)
Pietro Perona
Pietro Perona California Institute of Technology
Andrew Zisserman
Andrew Zisserman University of Oxford
Dilip Krishnan
Dilip Krishnan Google (United States)
Yann LeCun
Yann LeCun Facebook (United States)
Joan Bruna
Joan Bruna New York University

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