World's Best Scientists 2026 revealed!
Aaron Courville

Aaron Courville

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Computer Science
Canada
2025

D-Index & Metrics

Computer Science

D-Index
95
Citations
183159
World Ranking
450
National Ranking
15

Research.com Recognitions

  • 2025 - Research.com Computer Science in Canada Leader Award
  • 2023 - Research.com Computer Science in Canada Leader Award
  • 2022 - Research.com Computer Science in Canada Leader Award

Overview

Aaron Courville is affiliated with the University of Montreal in Canada, where they focus their research primarily in the field of Computer Science. Their work spans multiple subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Statistical and Nonlinear Physics, and Structural Biology.

Their research topics cover a broad range of themes such as Domain Adaptation and Few-Shot Learning, Reinforcement Learning in Robotics, Topic Modeling, Multimodal Machine Learning Applications, Generative Adversarial Networks and Image Synthesis, and Adversarial Robustness in Machine Learning.

Notable recent publications by Aaron Courville include:

  • Spirit In the Shell: A Mathematically Plausible Pathway from Emotions to Metacognition in Artificial Intelligence Systems, 2025, Zenodo (CERN European Organization for Nuclear Research)
  • Generative adversarial networks, 2020, Communications of the ACM
  • Static Analysis of Shape in TensorFlow Programs, 2020, arXiv (Cornell University)
  • Out-of-Distribution Generalization via Risk Extrapolation (REx), 2020, arXiv (Cornell University)
  • Data-Efficient Reinforcement Learning with Self-Predictive Representations, 2020, arXiv (Cornell University)

Aaron Courville frequently collaborates with several other researchers. Their frequent coauthors include:

  • Pablo Samuel Castro
  • Yoshua Bengio
  • Max Schwarzer
  • Johan Obando-Ceron
  • Alessandro Sordoni

Their work has been published extensively in various venues, with prominent publications appearing in:

  • arXiv (Cornell University)
  • Microscopy and Microanalysis
  • Communications of the ACM
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Journal of High Energy Physics

Best Publications

  • Generative Adversarial Nets

    Ian Goodfellow;Jean Pouget-Abadie;Mehdi Mirza;Bing Xu

  • Deep Learning

    Ian Goodfellow;Yoshua Bengio;Aaron Courville

  • Generative adversarial networks

    Ian Goodfellow;Jean Pouget-Abadie;Mehdi Mirza;Bing Xu

  • Representation Learning: A Review and New Perspectives

    Y. Bengio;A. Courville;P. Vincent

  • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

    Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho

  • Improved training of wasserstein GANs

    Ishaan Gulrajani;Faruk Ahmed;Martin Arjovsky;Vincent Dumoulin

  • Brain tumor segmentation with Deep Neural Networks

    Mohammad Havaei;Axel Davy;David Warde-Farley;Antoine Biard

  • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

    Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho

  • Why Does Unsupervised Pre-training Help Deep Learning?

    Dumitru Erhan;Aaron C. Courville;Yoshua Bengio;Pascal Vincent

  • Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

    Yoshua Bengio;Nicholas Léonard;Aaron C. Courville

  • Maxout Networks

    Ian Goodfellow;David Warde-Farley;Mehdi Mirza;Aaron Courville

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • Why Does Unsupervised Pre-training Help Deep Learning?

    Dumitru Erhan;Yoshua Bengio;Aaron Courville;Pierre-Antoine Manzagol

  • Building end-to-end dialogue systems using generative hierarchical neural network models

    Iulian V. Serban;Alessandro Sordoni;Yoshua Bengio;Aaron Courville

  • FiLM: Visual Reasoning with a General Conditioning Layer

    Ethan Perez;Ethan Perez;Florian Strub;Harm de Vries;Vincent Dumoulin

  • Challenges in Representation Learning: A Report on Three Machine Learning Contests

    Ian J. Goodfellow;Dumitru Erhan;Pierre Luc Carrier;Aaron Courville

  • Adversarially Learned Inference

    Vincent Dumoulin;Ishmael Belghazi;Ben Poole;Alex Lamb

  • An empirical evaluation of deep architectures on problems with many factors of variation

    Hugo Larochelle;Dumitru Erhan;Aaron Courville;James Bergstra

  • Describing Videos by Exploiting Temporal Structure

    Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas

  • Adversarially Learned Inference

    Vincent Dumoulin;Ishmael Belghazi;Ben Poole;Olivier Mastropietro

  • Challenges in representation learning

    Ian J. Goodfellow;Dumitru Erhan;Pierre Luc Carrier;Aaron Courville

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Chris Pal
Chris Pal Polytechnique Montréal
Ian Goodfellow
Ian Goodfellow Google (United States)
Nicolas Ballas
Nicolas Ballas Facebook (United States)
Hugo Larochelle
Hugo Larochelle Google (United States)
Joelle Pineau
Joelle Pineau McGill University
Pascal Vincent
Pascal Vincent Facebook (United States)
Kyunghyun Cho
Kyunghyun Cho New York University
Olivier Pietquin
Olivier Pietquin Google (United States)
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)

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