Aaron Courville

Publications

Haptics-based Curiosity for Sparse-reward Tasks. Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro. At Conference on Robot Learning (CoRL), 2022.

On the Compositional Generalization Gap of In-Context Learning. Dzmitry Bahdanau, Arian Hosseini, Aaron Courville, Alessandro Sordoni, Ankit Vani. At Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation. Sai Rajeswar Mudumba, Issam H. Laradji, Pau Rodriguez, David Vazquez, Aaron Courville. At British Machine Vision Conference (BMVC), 2022.

Countering Language Drift with KL Regularization. Issam H. Laradji, Michael Noukhovitch, Aaron Courville. At Workshop on Interactive Learning for Natural Language Processing (NeurIPS Workshop), 2022.

Unsupervised Model-based Pre-training for Data-efficient Reinforcement Learning from Pixels. Sai Rajeswar Mudumba, Pietro Mazzaglia, Tim Verbelen, Alexandre Piche, Aaron Courville, Alexandre Lacoste. At Workshop at the International Conference on Machine Learning (ICML), 2022.

Multi-label Iterated Learning for Image Classification with Label Ambiguity. Sai Rajeswar Mudumba, Pau Rodriguez, Soumye Singhal, David Vazquez, Aaron Courville. At Computer Vision and Pattern Recognition (CVPR), 2022.

VIM: Variational Independent Modules for Video Prediction. Rim Assouel, Lluis Castrejon, Aaron Courville, Nicolas Ballas, Yoshua Bengio. At Causal Learning and Reasoning (CLeaR), 2022.

Generative Compositional Augmentations for Scene Graph Prediction. Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky. At International Conference on Computer Vision (ICCV), 2021.

Understanding by Understanding Not: Modeling Negation in Language Models. Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, Aaron Courville. At North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

Unsupervised Learning of Dense Visual Representations. Pedro O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron Courville. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

Graph-Density Aware Losses for Novel Compositions in Scene Graph Generation. Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham Taylor, Aaron Courville, Eugene Belilovsky. At Britsh Machine Vision Conference (BMVC), 2020.

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation. Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang. At International Conference on Machine Learning (ICML), 2020.

Stochastic Neural Network with Kronecker Flow. Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

CLOSURE: Assessing Systematic Generalization of CLEVR models. Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, Aaron Courville. At Workshop at the Neural Information Processing Systems (NeurIPS), 2019.

Adversarial Computation of Optimal Transport Maps. Jacob Leygonie, Jennifer She, Amjad Almahairi, Sai Rajeswar Mudumba, Aaron Courville. At ArXiv, 2019.

Hierarchical Importance Weighted Autoencoders. Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville. At International Conference on Machine Learning (ICML), 2019.

Stochastic Neural Network with Kronecker Flow. Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville. At Workshop at the International Conference on Machine Learning (ICML), 2019.

Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation. Sai Rajeswar Mudumba, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vazquez, Derek Nowrouzezahrai, Aaron Courville. At International Journal in Computer Vision (IJCV), 2019.

On Difficulties of Probability Distillation. Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville. At International Conference on Learning Representations (ICLR), 2019.

Systematic Generalization: What Is Required and Can It Be Learned?. Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville. At International Conference on Learning Representations (ICLR), 2019.

Improving Explorability in Variational Inference with Annealed Variational Objectives. Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

Towards Text Generation with Adversarially Learned Neural Outlines. Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron Courville, Christopher Pal. At Conference on Neural Information Processing Systems (NeurIPS), 2018.

Hierarchical Adversarially Learned Inference. Ishmael Belghazi, Sai Rajeswar Mudumba, Olivier Mastropietro, Negar Rostamzadeh, Jovana Mitrovic, Aaron Courville. At Workshop at the International Conference on Machine Learning (ICML), 2018.

Neural Autoregressive Flows. Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville. At International Conference on Machine Learning (ICML), 2018.

Bayesian Hypernetworks. David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron Courville. At Workshop at the Neural Information Processing Systems (NeurIPS), 2017.