World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
16428
World Ranking
3384
National Ranking
1639

Overview

Michael C. Mozer is affiliated with Google in the United States and has an extensive research record in the field of Computer Science, with a focus on Artificial Intelligence. Their work spans multiple subfields including Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, and Cognitive Neuroscience, among others.

The scientist's research topics exhibit significant breadth, covering Domain Adaptation and Few-Shot Learning, Adversarial Robustness in Machine Learning, Neural Networks and Applications, Multimodal Machine Learning Applications, Topic Modeling, Advanced Graph Neural Networks, and Machine Learning and Data Classification.

Notable recent publications include:

  • Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure (2021), Computational Brain & Behavior
  • SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos (2022), arXiv (Cornell University)
  • von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning (2021), 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Mitigating Bias in Calibration Error Estimation (2020), arXiv (Cornell University)
  • Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules (2020), arXiv (Cornell University)

Michael C. Mozer has coauthored numerous publications with several frequent collaborators. Among these are Anirudh Goyal, Yoshua Bengio, Alex Lamb, Nan Rosemary Ke, and Tyler R. Scott.

The scientist's work has been published predominantly in arXiv (Cornell University), with over 50 publications, as well as in venues such as Nature Communications, Computational Brain & Behavior, the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), and Cognitive Science.

Best Publications

  • Bayesian community-wide culture-independent microbial source tracking

    Dan Knights;Justin Kuczynski;Emily S. Charlson;Jesse Zaneveld

  • Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment

    Michael C. Mozer;Paul Smolensky

  • The Neural Network House: An Environment that Adapts to its Inhabitants

    Michael C. Mozer

  • Deep neural network improves fracture detection by clinicians.

    Robert V. Lindsey;Aaron Daluiski;Sumit Chopra;Alexander Lachapelle

  • Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry

    M.C. Mozer;R. Wolniewicz;D.B. Grimes;E. Johnson

  • Neural net architectures for temporal sequence processing

    Michael C. Mozer

  • Optimizing distributed practice: theoretical analysis and practical implications.

    Nicholas J Cepeda;Noriko Coburn;Doug Rohrer;John T Wixted

  • Using Relevance to Reduce Network Size Automatically

    Michael C. Mozer;Paul Smolensky

  • An Intelligent Environment Must Be Adaptive

    M.C. Mozer

  • The Perception of Multiple Objects: A Connectionist Approach

    Michael C. Mozer

  • Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney statistic

    Lian Yan;Robert Dodier;Michael C. Mozer;Richard Wolniewicz

  • Object-Based Attention and Occlusion Evidence From Normal Participants and a Computational Model

    Marlene Behrmann;Richard S. Zemel;Michael C. Mozer

  • Improving Students’ Long-Term Knowledge Retention Through Personalized Review

    Robert V. Lindsey;Jeffery D. Shroyer;Harold Pashler;Michael C. Mozer

  • Neural network music composition by prediction: exploring the benefits of psychoacoustic constraints and multi-scale processing

    Michael C. Mozer

  • Advances in Neural Information Processing Systems 5

    Richard S Zemel;Christopher Williams;Michael C Mozer

  • Lessons from an Adaptive Home

    Michael C. Mozer

  • Induction of Multiscale Temporal Structure

    Michael C Mozer

  • A Focused Backpropagation Algorithm for Temporal Pattern Recognition.

    Michael C. Mozer

  • Evidence-based static branch prediction using machine learning

    Brad Calder;Dirk Grunwald;Michael Jones;Donald Lindsay

  • How Deep is Knowledge Tracing

    Mohammad Khajah;Robert V. Lindsey;Michael C. Mozer

  • Learning to generate images with perceptual similarity metrics

    Jake Snell;Karl Ridgeway;Renjie Liao;Brett D. Roads

Frequent Co-Authors

Harold Pashler
Harold Pashler University of California, San Diego
Richard S. Zemel
Richard S. Zemel University of Toronto
Yoshua Bengio
Yoshua Bengio University of Montreal
Paul Smolensky
Paul Smolensky Microsoft (United States)
Marlene Behrmann
Marlene Behrmann Carnegie Mellon University
Sepp Hochreiter
Sepp Hochreiter Johannes Kepler University of Linz
Shaun P. Vecera
Shaun P. Vecera University of Iowa
Dan Knights
Dan Knights University of Minnesota
Samy Bengio
Samy Bengio Apple (United States)
Tim Curran
Tim Curran University of Colorado Boulder

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