World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
13717
World Ranking
6691
National Ranking
265

Overview

Roger Grosse is affiliated with the University of Toronto in Canada. Their research predominantly spans the field of computer science, with a strong focus on artificial intelligence. They have contributed extensively to subfields including artificial intelligence, computer vision and pattern recognition, statistical and nonlinear physics, statistics and probability, and computational mechanics.

The scientific topics covered in Roger Grosse's work encompass several areas such as topic modeling, stochastic gradient optimization techniques, domain adaptation and few-shot learning, neural networks and applications, model reduction and neural networks, natural language processing techniques, and machine learning and algorithms.

Roger Grosse has published numerous papers in various venues, with a significant number in arXiv (Cornell University). Other publication venues include the Proceedings of the AAAI Conference on Artificial Intelligence and Avian Conservation and Ecology. Some selected recent papers are:

  • Importance weighted autoencoders, 2024, arXiv (Cornell University)
  • Picking Winning Tickets Before Training by Preserving Gradient Flow, 2020, arXiv (Cornell University)
  • Discovering Language Model Behaviors with Model-Written Evaluations, 2022, arXiv (Cornell University)
  • Toy Models of Superposition, 2022, arXiv (Cornell University)
  • The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning, 2020, arXiv (Cornell University)

Frequent co-authors in Roger Grosse's work include Juhan Bae, Yuhuai Wu, Jared Kaplan, Cem Anil, and Ethan Perez.

Best Publications

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

    Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng

  • Importance Weighted Autoencoders

    Yuri Burda;Roger Grosse;Ruslan Salakhutdinov

  • Isolating Sources of Disentanglement in Variational Autoencoders.

    Tian Qi Chen;Xuechen Li;Roger B. Grosse;David Duvenaud

  • Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

    Yuhuai Wu;Elman Mansimov;Shun Liao;Roger Grosse

  • Optimizing Neural Networks with Kronecker-factored Approximate Curvature

    James Martens;Roger Grosse

  • Ground truth dataset and baseline evaluations for intrinsic image algorithms

    Roger Grosse;Micah K. Johnson;Edward H. Adelson;William T. Freeman

  • Structure Discovery in Nonparametric Regression through Compositional Kernel Search

    David Duvenaud;James Lloyd;Roger Grosse;Joshua Tenenbaum

  • Unsupervised learning of hierarchical representations with convolutional deep belief networks

    Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng

  • Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

    Yuhuai Wu;Elman Mansimov;Roger B. Grosse;Shun Liao

  • Isolating Sources of Disentanglement in Variational Autoencoders

    Ricky T. Q. Chen;Xuechen Li;Roger Grosse;David Duvenaud

  • Structure Discovery in Nonparametric Regression through Compositional Kernel Search

    David Duvenaud;James Robert Lloyd;Roger Grosse;Joshua B. Tenenbaum

  • The Reversible Residual Network: Backpropagation Without Storing Activations

    Aidan N. Gomez;Mengye Ren;Raquel Urtasun;Roger B. Grosse

  • Picking Winning Tickets Before Training by Preserving Gradient Flow

    Chaoqi Wang;Guodong Zhang;Roger Grosse

  • Shift-invariant sparse coding for audio classification

    Roger Grosse;Rajat Raina;Helen Kwong;Andrew Y. Ng

  • Automatic construction and natural-language description of nonparametric regression models

    James Robert Lloyd;David Duvenaud;Roger Grosse;Joshua B. Tenenbaum

  • Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

    Yeming Wen;Paul Vicol;Jimmy Ba;Dustin Tran

  • On the Quantitative Analysis of Decoder-Based Generative Models

    Yuhuai Wu;Yuri Burda;Ruslan Salakhutdinov;Roger B. Grosse

  • FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS

    Shengyang Sun;Guodong Zhang;Jiaxin Shi;Roger B. Grosse

  • A Kronecker-factored approximate fisher matrix for convolution layers

    Roger Grosse;James Martens

  • Sorting out Lipschitz function approximation

    Cem Anil;James Lucas;Roger B. Grosse

  • Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions.

    Matthew MacKay;Paul Vicol;Jonathan Lorraine;David Duvenaud

  • Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse

    James Lucas;George Tucker;Roger B. Grosse;Mohammad Norouzi

Frequent Co-Authors

Jimmy Ba
Jimmy Ba University of Toronto
David Duvenaud
David Duvenaud University of Toronto
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University
Richard S. Zemel
Richard S. Zemel University of Toronto
Andrew Y. Ng
Andrew Y. Ng Stanford University
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Mohammad Norouzi
Mohammad Norouzi Google (United States)
George Tucker
George Tucker Google (United States)

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