Modeling Temporal Structure in Classical Conditioning. - Aaron C. Courville; David S. Touretzky (NIPS, 2001)

Timing and Partial Observability in the Dopamine System. - Nathaniel D. Daw; Aaron C. Courville; David S. Touretzky (NIPS, 2002)

Model Uncertainty in Classical Conditioning. - Aaron C. Courville; Nathaniel D. Daw; Geoffrey J. Gordon; David S. Touretzky (NIPS, 2003)

Similarity and Discrimination in Classical Conditioning: A Latent Variable Account. - Aaron C. Courville; Nathaniel D. Daw; David S. Touretzky (NIPS, 2004)

Interacting Markov Random Fields for Simultaneous Terrain Modeling and Obstacle Detection. - Carl Wellington; Aaron C. Courville; Anthony Stentz (Robotics: Science and Systems, 2005)

A Generative Model of Terrain for Autonomous Navigation in Vegetation. - Carl Wellington; Aaron C. Courville; Anthony Stentz (Int. J. Robotics Res., 2006)

Representation and Timing in Theories of the Dopamine System. - Nathaniel D. Daw; Aaron C. Courville; David S. Touretzky (Neural Comput., 2006)

An empirical evaluation of deep architectures on problems with many factors of variation. - Hugo Larochelle; Dumitru Erhan; Aaron C. Courville; James Bergstra; Yoshua Bengio (ICML, 2007)

The rat as particle filter. - Nathaniel D. Daw; Aaron C. Courville (NIPS, 2007)

An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism. - Aaron C. Courville; Douglas Eck; Yoshua Bengio (NIPS, 2009)

Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio (CoRR, 2010)

Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines. - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio; Pascal Vincent; Olivier Delalleau (AISTATS, 2010)

Why Does Unsupervised Pre-training Help Deep Learning? - Dumitru Erhan; Yoshua Bengio; Aaron C. Courville; Pierre-Antoine Manzagol; Pascal Vincent; Samy Bengio (J. Mach. Learn. Res., 2010)

Why Does Unsupervised Pre-training Help Deep Learning? - Dumitru Erhan; Aaron C. Courville; Yoshua Bengio; Pascal Vincent (AISTATS, 2010)

Unsupervised Models of Images by Spikeand-Slab RBMs. - Aaron C. Courville; James Bergstra; Yoshua Bengio (ICML, 2011)

On Tracking The Partition Function. - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio (NIPS, 2011)

The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All) - James Bergstra; Aaron C. Courville; Yoshua Bengio (CoRR, 2011)

A Spike and Slab Restricted Boltzmann Machine. - Aaron C. Courville; James Bergstra; Yoshua Bengio (AISTATS, 2011)

A bistable computational model of recurring epileptiform activity as observed in rodent slice preparations. - Robert D. Vincent; Aaron C. Courville; Joelle Pineau (Neural Networks, 2011)

Disentangling Factors of Variation for Facial Expression Recognition. - Salah Rifai; Yoshua Bengio; Aaron C. Courville; Pascal Vincent; Mehdi Mirza (ECCV (6), 2012)

Large-Scale Feature Learning With Spike-and-Slab Sparse Coding. - Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (ICML, 2012)

Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery - Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (CoRR, 2012)

On Training Deep Boltzmann Machines - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio (CoRR, 2012)

Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives - Yoshua Bengio; Aaron C. Courville; Pascal Vincent (CoRR, 2012)

Efficient EM Training of Gaussian Mixtures with Missing Data - Olivier Delalleau; Aaron C. Courville; Yoshua Bengio (CoRR, 2012)

Disentangling Factors of Variation via Generative Entangling - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio (CoRR, 2012)

Joint Training of Deep Boltzmann Machines - Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (CoRR, 2012)

Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. - Grégoire Mesnil; Yann N. Dauphin; Xavier Glorot; Salah Rifai; Yoshua Bengio; Ian J. Goodfellow; Erick Lavoie; Xavier Muller; Guillaume Desjardins; David Warde-Farley; Pascal Vincent; Aaron C. Courville; et al. (ICML Unsupervised and Transfer Learning, 2012)

Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions. - Heng Luo; Pierre Luc Carrier; Aaron C. Courville; Yoshua Bengio (AISTATS, 2013)

Combining modality specific deep neural networks for emotion recognition in video. - Samira Ebrahimi Kahou; Christopher J. Pal; Xavier Bouthillier; Pierre Froumenty; Çaglar Gülçehre; Roland Memisevic; Pascal Vincent; Aaron C. Courville; Yoshua Bengio; Raul Chandias Ferrari; Mehdi Mirza; Sébastien Jean; et al. (ICMI, 2013)

Maxout Networks. - Ian J. Goodfellow; David Warde-Farley; Mehdi Mirza; Aaron C. Courville; Yoshua Bengio (ICML (3), 2013)

Challenges in Representation Learning: A Report on Three Machine Learning Contests. - Ian J. Goodfellow; Dumitru Erhan; Pierre Luc Carrier; Aaron C. Courville; Mehdi Mirza; Benjamin Hamner; William Cukierski; Yichuan Tang; David Thaler; Dong-Hyun Lee; Yingbo Zhou; Chetan Ramaiah; et al. (ICONIP (3), 2013)

Multi-Prediction Deep Boltzmann Machines. - Ian J. Goodfellow; Mehdi Mirza; Aaron C. Courville; Yoshua Bengio (NIPS, 2013)

Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. - Yoshua Bengio; Nicholas Léonard; Aaron C. Courville (CoRR, 2013)

Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines - Guillaume Desjardins; Razvan Pascanu; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2013)

Joint Training Deep Boltzmann Machines for Classification - Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (ICLR (Workshop), 2013)

Representation Learning: A Review and New Perspectives. - Yoshua Bengio; Aaron C. Courville; Pascal Vincent (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning. - Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Evaluating and Extending Trajectory Features for Activity Recognition. - Ross Messing; Atousa Torabi; Aaron C. Courville (Advanced Topics in Computer Vision, 2013)

Deep Learning of Representations. - Yoshua Bengio; Aaron C. Courville (Handbook on Neural Information Processing, 2013)

On the Challenges of Physical Implementations of RBMs. - Vincent Dumoulin; Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (AAAI, 2014)

Generative Adversarial Nets. - Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron C. Courville; Yoshua Bengio (NIPS, 2014)

Deep Tempering. - Guillaume Desjardins; Heng Luo; Aaron C. Courville; Yoshua Bengio (CoRR, 2014)

An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks. - Ian J. Goodfellow; Mehdi Mirza; Xia Da; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2014)

An empirical analysis of dropout in piecewise linear networks. - David Warde-Farley; Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2014)

The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions. - Aaron C. Courville; Guillaume Desjardins; James Bergstra; Yoshua Bengio (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Describing Videos by Exploiting Temporal Structure. - Li Yao; Atousa Torabi; Kyunghyun Cho; Nicolas Ballas; Christopher J. Pal; Hugo Larochelle; Aaron C. Courville (ICCV, 2015)

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. - Kelvin Xu; Jimmy Ba; Ryan Kiros; Kyunghyun Cho; Aaron C. Courville; Ruslan Salakhutdinov; Richard S. Zemel; Yoshua Bengio (ICML, 2015)

A Recurrent Latent Variable Model for Sequential Data. - Junyoung Chung; Kyle Kastner; Laurent Dinh; Kratarth Goel; Aaron C. Courville; Yoshua Bengio (NIPS, 2015)

Learning Distributed Representations from Reviews for Collaborative Filtering. - Amjad Almahairi; Kyle Kastner; Kyunghyun Cho; Aaron C. Courville (RecSys, 2015)

Variance Reduction in SGD by Distributed Importance Sampling. - Guillaume Alain; Alex Lamb; Chinnadhurai Sankar; Aaron C. Courville; Yoshua Bengio (CoRR, 2015)

Task Loss Estimation for Sequence Prediction. - Dzmitry Bahdanau; Dmitriy Serdyuk; Philemon Brakel; Nan Rosemary Ke; Jan Chorowski; Aaron C. Courville; Yoshua Bengio (CoRR, 2015)

A Controller Recognizer Framework: How necessary is recognition for control? - Marcin Moczulski; Kelvin Xu; Aaron C. Courville; KyungHyun Cho (CoRR, 2015)

Hierarchical Neural Network Generative Models for Movie Dialogues. - Iulian Vlad Serban; Alessandro Sordoni; Yoshua Bengio; Aaron C. Courville; Joelle Pineau (CoRR, 2015)

Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research. - Atousa Torabi; Christopher J. Pal; Hugo Larochelle; Aaron C. Courville (CoRR, 2015)

ReSeg: A Recurrent Neural Network for Object Segmentation. - Francesco Visin; Kyle Kastner; Aaron C. Courville; Yoshua Bengio; Matteo Matteucci; KyungHyun Cho (CoRR, 2015)

ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks. - Francesco Visin; Kyle Kastner; Kyunghyun Cho; Matteo Matteucci; Aaron C. Courville; Yoshua Bengio (CoRR, 2015)

Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism. - Li Yao; Atousa Torabi; Kyunghyun Cho; Nicolas Ballas; Christopher Joseph Pal; Hugo Larochelle; Aaron C. Courville (CoRR, 2015)

Challenges in representation learning: A report on three machine learning contests. - Ian J. Goodfellow; Dumitru Erhan; Pierre Luc Carrier; Aaron C. Courville; Mehdi Mirza; Benjamin Hamner; William Cukierski; Yichuan Tang; David Thaler; Dong-Hyun Lee; Yingbo Zhou; Chetan Ramaiah; et al. (Neural Networks, 2015)

Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks. - Kyunghyun Cho; Aaron C. Courville; Yoshua Bengio (IEEE Trans. Multim., 2015)

Deep Learning. - Ian J. Goodfellow; Yoshua Bengio; Aaron C. Courville (MIT Press, 2016)

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. - Iulian Vlad Serban; Alessandro Sordoni; Yoshua Bengio; Aaron C. Courville; Joelle Pineau (AAAI, 2016)

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus. - Iulian Vlad Serban; Alberto García-Durán; Çaglar Gülçehre; Sungjin Ahn; Sarath Chandar; Aaron C. Courville; Yoshua Bengio (ACL (1), 2016)

ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation. - Francesco Visin; Adriana Romero; Kyunghyun Cho; Matteo Matteucci; Marco Ciccone; Kyle Kastner; Yoshua Bengio; Aaron C. Courville (CVPR Workshops, 2016)

Deep Learning Vector Quantization. - Harm de Vries; Roland Memisevic; Aaron C. Courville (ESANN, 2016)

Dynamic Capacity Networks. - Amjad Almahairi; Nicolas Ballas; Tim Cooijmans; Yin Zheng; Hugo Larochelle; Aaron C. Courville (ICML, 2016)

Deconstructing the Ladder Network Architecture. - Mohammad Pezeshki; Linxi Fan; Philemon Brakel; Aaron C. Courville; Yoshua Bengio (ICML, 2016)

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. - Ying Zhang 033; Mohammad Pezeshki; Philémon Brakel; Saizheng Zhang; César Laurent; Yoshua Bengio; Aaron C. Courville (INTERSPEECH, 2016)

Professor Forcing: A New Algorithm for Training Recurrent Networks. - Anirudh Goyal; Alex Lamb; Ying Zhang 033; Saizheng Zhang; Aaron C. Courville; Yoshua Bengio (NIPS, 2016)

Theano: A Python framework for fast computation of mathematical expressions. - Rami Al-Rfou; Guillaume Alain; Amjad Almahairi; Christof Angermüller; Dzmitry Bahdanau; Nicolas Ballas; Frédéric Bastien; Justin Bayer; Anatoly Belikov; Alexander Belopolsky; Yoshua Bengio; Arnaud Bergeron; et al. (CoRR, 2016)

First Result on Arabic Neural Machine Translation. - Amjad Almahairi; Kyunghyun Cho; Nizar Habash; Aaron C. Courville (CoRR, 2016)

Delving Deeper into Convolutional Networks for Learning Video Representations. - Nicolas Ballas; Li Yao; Chris Pal; Aaron C. Courville (ICLR (Poster), 2016)

Recurrent Batch Normalization. - Tim Cooijmans; Nicolas Ballas; César Laurent; Aaron C. Courville (CoRR, 2016)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Hugo Larochelle; Aaron C. Courville; Chris Pal (CoRR, 2016)

Discriminative Regularization for Generative Models. - Alex Lamb; Vincent Dumoulin; Aaron C. Courville (CoRR, 2016)

A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. - Tegan Maharaj; Nicolas Ballas; Aaron C. Courville; Christopher Joseph Pal (CoRR, 2016)

Multi-modal Variational Encoder-Decoders. - Iulian Vlad Serban; Alexander G. Ororbia II; Joelle Pineau; Aaron C. Courville (CoRR, 2016)

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images. - David Vázquez 001; Jorge Bernal; Francisco Javier Sánchez; Gloria Fernández-Esparrach; Antonio M. López 001; Adriana Romero; Michal Drozdzal; Aaron C. Courville (CoRR, 2016)

EmoNets: Multimodal deep learning approaches for emotion recognition in video. - Samira Ebrahimi Kahou; Xavier Bouthillier; Pascal Lamblin; Çaglar Gülçehre; Vincent Michalski; Kishore Konda; Sébastien Jean; Pierre Froumenty; Yann N. Dauphin; Nicolas Boulanger-Lewandowski; Raul Chandias Ferrari; Mehdi Mirza; et al. (J. Multimodal User Interfaces, 2016)

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation. - Iulian Vlad Serban; Tim Klinger; Gerald Tesauro; Kartik Talamadupula; Bowen Zhou 002; Yoshua Bengio; Aaron C. Courville (AAAI, 2017)

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. - Iulian Vlad Serban; Alessandro Sordoni; Ryan Lowe; Laurent Charlin; Joelle Pineau; Aaron C. Courville; Yoshua Bengio (AAAI, 2017)

A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering. - Tegan Maharaj; Nicolas Ballas; Anna Rohrbach; Aaron C. Courville; Christopher Joseph Pal (CVPR, 2017)

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue. - Harm de Vries; Florian Strub; Sarath Chandar; Olivier Pietquin; Hugo Larochelle; Aaron C. Courville (CVPR, 2017)

Piecewise Latent Variables for Neural Variational Text Processing. - Iulian Vlad Serban; Alexander Ororbia; Joelle Pineau; Aaron C. Courville (SPNLP@EMNLP, 2017)

An Actor-Critic Algorithm for Sequence Prediction. - Dzmitry Bahdanau; Philemon Brakel; Kelvin Xu; Anirudh Goyal; Ryan Lowe; Joelle Pineau; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2017)

Recurrent Batch Normalization. - Tim Cooijmans; Nicolas Ballas; César Laurent; Çaglar Gülçehre; Aaron C. Courville (ICLR (Poster), 2017)

Calibrating Energy-based Generative Adversarial Networks. - Zihang Dai; Amjad Almahairi; Philip Bachman; Eduard H. Hovy; Aaron C. Courville (ICLR (Poster), 2017)

Adversarially Learned Inference. - Vincent Dumoulin; Ishmael Belghazi; Ben Poole; Alex Lamb; Martín Arjovsky; Olivier Mastropietro; Aaron C. Courville (ICLR (Poster), 2017)

PixelVAE: A Latent Variable Model for Natural Images. - Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taïga; Francesco Visin; David Vázquez 001; Aaron C. Courville (ICLR (Poster), 2017)

Deep Nets Don't Learn via Memorization. - David Krueger 001; Nicolas Ballas; Stanislaw Jastrzebski; Devansh Arpit; Maxinder S. Kanwal; Tegan Maharaj; Emmanuel Bengio; Asja Fischer; Aaron C. Courville (ICLR (Workshop), 2017)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Aaron C. Courville; Christopher J. Pal (ICLR (Poster), 2017)

SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. - Soroush Mehri; Kundan Kumar; Ishaan Gulrajani; Rithesh Kumar; Shubham Jain; Jose Sotelo; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2017)

Generalizable Features From Unsupervised Learning. - Mehdi Mirza; Aaron C. Courville; Yoshua Bengio (ICLR (Workshop), 2017)

Char2Wav: End-to-End Speech Synthesis. - Jose Sotelo; Soroush Mehri; Kundan Kumar; João Felipe Santos; Kyle Kastner; Aaron C. Courville; Yoshua Bengio (ICLR (Workshop), 2017)

A Closer Look at Memorization in Deep Networks. - Devansh Arpit; Stanislaw Jastrzebski; Nicolas Ballas; David Krueger 001; Emmanuel Bengio; Maxinder S. Kanwal; Tegan Maharaj; Asja Fischer; Aaron C. Courville; Yoshua Bengio; Simon Lacoste-Julien (ICML, 2017)

End-to-end optimization of goal-driven and visually grounded dialogue systems. - Florian Strub; Harm de Vries; Jérémie Mary; Bilal Piot; Aaron C. Courville; Olivier Pietquin (IJCAI, 2017)

Counterpoint by Convolution. - Cheng-Zhi Anna Huang; Tim Cooijmans; Adam Roberts; Aaron C. Courville; Douglas Eck (ISMIR, 2017)

Improved Training of Wasserstein GANs. - Ishaan Gulrajani; Faruk Ahmed; Martín Arjovsky; Vincent Dumoulin; Aaron C. Courville (NIPS, 2017)

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. - Alex Lamb; R. Devon Hjelm; Yaroslav Ganin; Joseph Paul Cohen; Aaron C. Courville; Yoshua Bengio (NIPS, 2017)

Modulating early visual processing by language. - Harm de Vries; Florian Strub; Jérémie Mary; Hugo Larochelle; Olivier Pietquin; Aaron C. Courville (NIPS, 2017)

Adversarial Generation of Natural Language. - Sandeep Subramanian; Sai Rajeswar; Francis Dutil; Chris Pal; Aaron C. Courville (Rep4NLP@ACL, 2017)

Learning Visual Reasoning Without Strong Priors. - Ethan Perez; Harm de Vries; Florian Strub; Vincent Dumoulin; Aaron C. Courville (CoRR, 2017)

Self-organized Hierarchical Softmax. - Yikang Shen; Shawn Tan; Christopher Joseph Pal; Aaron C. Courville (CoRR, 2017)

Learnable Explicit Density for Continuous Latent Space and Variational Inference. - Chin-Wei Huang; Ahmed Touati; Laurent Dinh; Michal Drozdzal; Mohammad Havaei; Laurent Charlin; Aaron C. Courville (CoRR, 2017)

Bayesian Hypernetworks. - David Krueger 001; Chin-Wei Huang; Riashat Islam; Ryan Turner; Alexandre Lacoste; Aaron C. Courville (CoRR, 2017)

Movie Description. - Anna Rohrbach; Atousa Torabi; Marcus Rohrbach; Niket Tandon; Christopher Joseph Pal; Hugo Larochelle; Aaron C. Courville; Bernt Schiele (Int. J. Comput. Vis., 2017)

Brain tumor segmentation with Deep Neural Networks. - Mohammad Havaei; Axel Davy; David Warde-Farley; Antoine Biard; Aaron C. Courville; Yoshua Bengio; Chris Pal; Pierre-Marc Jodoin; Hugo Larochelle (Medical Image Anal., 2017)

FiLM: Visual Reasoning with a General Conditioning Layer. - Ethan Perez; Florian Strub; Harm de Vries; Vincent Dumoulin; Aaron C. Courville (AAAI, 2018)

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance. - Yikang Shen; Zhouhan Lin; Athul Paul Jacob; Alessandro Sordoni; Aaron C. Courville; Yoshua Bengio (ACL (1), 2018)

Sim-to-Real Transfer with Neural-Augmented Robot Simulation. - Florian Golemo; Adrien Ali Taïga; Aaron C. Courville; Pierre-Yves Oudeyer (CoRL, 2018)

Visual Reasoning with Multi-hop Feature Modulation. - Florian Strub; Mathieu Seurin; Ethan Perez; Harm de Vries; Jérémie Mary; Philippe Preux; Aaron C. Courville; Olivier Pietquin (ECCV (5), 2018)

HoME: a Household Multimodal Environment. - Simon Brodeur; Ethan Perez; Ankesh Anand; Florian Golemo; Luca Celotti; Florian Strub; Jean Rouat; Hugo Larochelle; Aaron C. Courville (ICLR (Workshop), 2018)

Neural Language Modeling by Jointly Learning Syntax and Lexicon. - Yikang Shen; Zhouhan Lin; Chin-Wei Huang; Aaron C. Courville (ICLR (Poster), 2018)

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. - Amjad Almahairi; Sai Rajeswar; Alessandro Sordoni; Philip Bachman; Aaron C. Courville (ICML, 2018)

Mutual Information Neural Estimation. - Mohamed Ishmael Belghazi; Aristide Baratin; Sai Rajeswar; Sherjil Ozair; Yoshua Bengio; R. Devon Hjelm; Aaron C. Courville (ICML, 2018)

Neural Autoregressive Flows. - Chin-Wei Huang; David Krueger 001; Alexandre Lacoste; Aaron C. Courville (ICML, 2018)

Improving Explorability in Variational Inference with Annealed Variational Objectives. - Chin-Wei Huang; Shawn Tan; Alexandre Lacoste; Aaron C. Courville (NeurIPS, 2018)

Towards Text Generation with Adversarially Learned Neural Outlines. - Sandeep Subramanian; Sai Rajeswar; Alessandro Sordoni; Adam Trischler; Aaron C. Courville; Chris Pal (NeurIPS, 2018)

MINE: Mutual Information Neural Estimation. - Ishmael Belghazi; Sai Rajeswar; Aristide Baratin; R. Devon Hjelm; Aaron C. Courville (CoRR, 2018)

Hierarchical Adversarially Learned Inference. - Mohamed Ishmael Belghazi; Sai Rajeswar; Olivier Mastropietro; Negar Rostamzadeh; Jovana Mitrovic; Aaron C. Courville (CoRR, 2018)

Generating Contradictory, Neutral, and Entailing Sentences. - Yikang Shen; Shawn Tan; Chin-Wei Huang; Aaron C. Courville (CoRR, 2018)

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. - Vikas Verma; Alex Lamb; Christopher Beckham; Aaron C. Courville; Ioannis Mitliagkas; Yoshua Bengio (CoRR, 2018)

On the Spectral Bias of Deep Neural Networks. - Nasim Rahaman; Devansh Arpit; Aristide Baratin; Felix Draxler; Min Lin; Fred A. Hamprecht; Yoshua Bengio; Aaron C. Courville (CoRR, 2018)

Approximate Exploration through State Abstraction. - Adrien Ali Taïga; Aaron C. Courville; Marc G. Bellemare (CoRR, 2018)

On the Learning Dynamics of Deep Neural Networks. - Remi Tachet des Combes; Mohammad Pezeshki; Samira Shabanian; Aaron C. Courville; Yoshua Bengio (CoRR, 2018)

Blindfold Baselines for Embodied QA. - Ankesh Anand; Eugene Belilovsky; Kyle Kastner; Hugo Larochelle; Aaron C. Courville (CoRR, 2018)

Harmonic Recomposition using Conditional Autoregressive Modeling. - Kyle Kastner; Rithesh Kumar; Tim Cooijmans; Aaron C. Courville (CoRR, 2018)

Planning in Dynamic Environments with Conditional Autoregressive Models. - Johanna Hansen; Kyle Kastner; Aaron C. Courville; Gregory Dudek (CoRR, 2018)

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. - Catalina Cangea; Eugene Belilovsky; Aaron C. Courville (BMVC, 2019)

Representation Mixing for TTS Synthesis. - Kyle Kastner; João Felipe Santos; Yoshua Bengio; Aaron C. Courville (ICASSP, 2019)

Batch Weight for Domain Adaptation With Mass Shift. - Mikolaj Binkowski; R. Devon Hjelm; Aaron C. Courville (ICCV, 2019)

Improved Conditional VRNNs for Video Prediction. - Lluís Castrejón; Nicolas Ballas; Aaron C. Courville (ICCV, 2019)

Systematic Generalization: What Is Required and Can It Be Learned? - Dzmitry Bahdanau; Shikhar Murty; Michael Noukhovitch; Thien Huu Nguyen; Harm de Vries; Aaron C. Courville (ICLR (Poster), 2019)

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. - Yikang Shen; Shawn Tan; Alessandro Sordoni; Aaron C. Courville (ICLR, 2019)

Hierarchical Importance Weighted Autoencoders. - Chin-Wei Huang; Kris Sankaran; Eeshan Dhekane; Alexandre Lacoste; Aaron C. Courville (ICML, 2019)

On the Spectral Bias of Neural Networks. - Nasim Rahaman; Aristide Baratin; Devansh Arpit; Felix Draxler; Min Lin; Fred A. Hamprecht; Yoshua Bengio; Aaron C. Courville (ICML, 2019)

Deep Generative Modeling of LiDAR Data. - Lucas Caccia; Herke van Hoof; Aaron C. Courville; Joelle Pineau (IROS, 2019)

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. - Catalina Cangea; Eugene Belilovsky; Pietro Liò; Aaron C. Courville (ViGIL@NeurIPS, 2019)

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis. - Kundan Kumar; Rithesh Kumar; Thibault de Boissiere; Lucas Gestin; Wei Zhen Teoh; Jose Sotelo; Alexandre de Brébisson; Yoshua Bengio; Aaron C. Courville (NeurIPS, 2019)

No-Press Diplomacy: Modeling Multi-Agent Gameplay. - Philip Paquette; Yuchen Lu; Steven Bocco; Max O. Smith; Satya Ortiz-Gagne; Jonathan K. Kummerfeld; Joelle Pineau; Satinder Singh 001; Aaron C. Courville (NeurIPS, 2019)

Ordered Memory. - Yikang Shen; Shawn Tan; Seyed Arian Hosseini; Zhouhan Lin; Alessandro Sordoni; Aaron C. Courville (NeurIPS, 2019)

CLOSURE: Assessing Systematic Generalization of CLEVR Models. - Harm de Vries; Dzmitry Bahdanau; Shikhar Murty; Aaron C. Courville; Philippe Beaudoin (ViGIL@NeurIPS, 2019)

Probability Distillation: A Caveat and Alternatives. - Chin-Wei Huang; Faruk Ahmed; Kundan Kumar; Alexandre Lacoste; Aaron C. Courville (UAI, 2019)

Maximum Entropy Generators for Energy-Based Models. - Rithesh Kumar; Anirudh Goyal; Aaron C. Courville; Yoshua Bengio (CoRR, 2019)

Note on the bias and variance of variational inference. - Chin-Wei Huang; Aaron C. Courville (CoRR, 2019)

Investigating Biases in Textual Entailment Datasets. - Shawn Tan; Yikang Shen; Chin-Wei Huang; Aaron C. Courville (CoRR, 2019)

Adversarial Computation of Optimal Transport Maps. - Jacob Leygonie; Jennifer She; Amjad Almahairi; Sai Rajeswar; Aaron C. Courville (CoRR, 2019)

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment. - Adrien Ali Taïga; William Fedus; Marlos C. Machado; Aaron C. Courville; Marc G. Bellemare (CoRR, 2019)

Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery. - Shawn Tan; Guillaume Androz; Ahmad Chamseddine; Pierre Fecteau; Aaron C. Courville; Yoshua Bengio; Joseph Paul Cohen (CoRR, 2019)

Selective Brain Damage: Measuring the Disparate Impact of Model Pruning. - Sara Hooker; Aaron C. Courville; Yann N. Dauphin; Andrea Frome (CoRR, 2019)

CLOSURE: Assessing Systematic Generalization of CLEVR Models. - Dzmitry Bahdanau; Harm de Vries; Timothy J. O'Donnell; Shikhar Murty; Philippe Beaudoin; Yoshua Bengio; Aaron C. Courville (CoRR, 2019)

Detecting Semantic Anomalies. - Faruk Ahmed; Aaron C. Courville (AAAI, 2020)

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. - Iulian Vlad Serban; Varun Gupta; Ekaterina Kochmar; Dung Do Vu; Robert Belfer; Joelle Pineau; Aaron C. Courville; Laurent Charlin; Yoshua Bengio (AIED (2), 2020)

Stochastic Neural Network with Kronecker Flow. - Chin-Wei Huang; Ahmed Touati; Pascal Vincent; Gintare Karolina Dziugaite; Alexandre Lacoste; Aaron C. Courville (AISTATS, 2020)

Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation. - Boris Knyazev 001; Harm de Vries; Catalina Cangea; Graham W. Taylor; Aaron C. Courville; Eugene Belilovsky (BMVC, 2020)

Supervised Seeded Iterated Learning for Interactive Language Learning. - Yuchen Lu; Soumye Singhal; Florian Strub; Olivier Pietquin; Aaron C. Courville (EMNLP (1), 2020)

Recursive Top-Down Production for Sentence Generation with Latent Trees. - Shawn Tan; Yikang Shen; Alessandro Sordoni; Aaron C. Courville; Timothy J. O'Donnell (EMNLP (Findings), 2020)

On Bonus Based Exploration Methods In The Arcade Learning Environment. - Adrien Ali Taïga; William Fedus; Marlos C. Machado; Aaron C. Courville; Marc G. Bellemare (ICLR, 2020)

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation. - Jae Hyun Lim 001; Aaron C. Courville; Christopher J. Pal; Chin-Wei Huang (ICML, 2020)

Countering Language Drift with Seeded Iterated Learning. - Yuchen Lu; Soumye Singhal; Florian Strub; Aaron C. Courville; Olivier Pietquin (ICML, 2020)

Unsupervised Learning of Dense Visual Representations. - Pedro O. Pinheiro; Amjad Almahairi; Ryan Y. Benmalek; Florian Golemo; Aaron C. Courville (NeurIPS, 2020)

Generative adversarial networks. - Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron C. Courville; Yoshua Bengio (Commun. ACM, 2020)

Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models. - Chin-Wei Huang; Laurent Dinh; Aaron C. Courville (CoRR, 2020)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Rémi Le Priol; Aaron C. Courville (CoRR, 2020)

Generative Graph Perturbations for Scene Graph Prediction. - Boris Knyazev 001; Harm de Vries; Catalina Cangea; Graham W. Taylor; Aaron C. Courville; Eugene Belilovsky (CoRR, 2020)

Data-Efficient Reinforcement Learning with Momentum Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (CoRR, 2020)

NU-GAN: High resolution neural upsampling with GAN. - Rithesh Kumar; Kundan Kumar; Vicki Anand; Yoshua Bengio; Aaron C. Courville (CoRR, 2020)

Explicitly Modeling Syntax in Language Model improves Generalization. - Yikang Shen; Shawn Tan; Alessandro Sordoni; Siva Reddy; Aaron C. Courville (CoRR, 2020)

Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation. - Sai Rajeswar; Fahim Mannan; Florian Golemo; Jérôme Parent-Lévesque; David Vázquez 001; Derek Nowrouzezahrai; Aaron C. Courville (Int. J. Comput. Vis., 2020)

StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling. - Yikang Shen; Yi Tay; Che Zheng; Dara Bahri; Donald Metzler; Aaron C. Courville (ACL/IJCNLP (1), 2021)

Emergent Communication under Competition. - Michael Noukhovitch; Travis LaCroix; Angeliki Lazaridou; Aaron C. Courville (AAMAS, 2021)

Haptics-based Curiosity for Sparse-reward Tasks. - Sai Rajeswar; Cyril Ibrahim; Nitin Surya; Florian Golemo; David Vázquez 001; Aaron C. Courville; Pedro O. Pinheiro (CoRL, 2021)

Generative Compositional Augmentations for Scene Graph Prediction. - Boris Knyazev 001; Harm de Vries; Catalina Cangea; Graham W. Taylor; Aaron C. Courville; Eugene Belilovsky (ICCV, 2021)

Systematic generalisation with group invariant predictions. - Faruk Ahmed; Yoshua Bengio; Harm van Seijen; Aaron C. Courville (ICLR, 2021)

Neural Approximate Sufficient Statistics for Implicit Models. - Yanzhi Chen; Dinghuai Zhang; Michael U. Gutmann; Aaron C. Courville; Zhanxing Zhu (ICLR, 2021)

Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization. - Chin-Wei Huang; Ricky T. Q. Chen; Christos Tsirigotis; Aaron C. Courville (ICLR, 2021)

Integrating Categorical Semantics into Unsupervised Domain Translation. - Samuel Lavoie-Marchildon; Faruk Ahmed; Aaron C. Courville (ICLR, 2021)

Learning Task Decomposition with Ordered Memory Policy Network. - Yuchen Lu; Yikang Shen; Siyuan Zhou; Aaron C. Courville; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2021)

Data-Efficient Reinforcement Learning with Self-Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (ICLR, 2021)

Iterated learning for emergent systematicity in VQA. - Ankit Vani; Max Schwarzer; Yuchen Lu; Eeshan Dhekane; Aaron C. Courville (ICLR, 2021)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Dinghuai Zhang; Rémi Le Priol; Aaron C. Courville (ICML, 2021)

Continuous Coordination As a Realistic Scenario for Lifelong Learning. - Hadi Nekoei; Akilesh Badrinaaraayanan; Aaron C. Courville; Sarath Chandar (ICML, 2021)

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? - Dinghuai Zhang; Kartik Ahuja; Yilun Xu; Yisen Wang 001; Aaron C. Courville (ICML, 2021)

Understanding by Understanding Not: Modeling Negation in Language Models. - Arian Hosseini; Siva Reddy; Dzmitry Bahdanau; R. Devon Hjelm; Alessandro Sordoni; Aaron C. Courville (NAACL-HLT, 2021)

Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle. - Yikang Shen; Shawn Tan; Alessandro Sordoni; Siva Reddy; Aaron C. Courville (NAACL-HLT, 2021)

Deep Reinforcement Learning at the Edge of the Statistical Precipice. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2021)

A Variational Perspective on Diffusion-Based Generative Models and Score Matching. - Chin-Wei Huang; Jae Hyun Lim 001; Aaron C. Courville (NeurIPS, 2021)

Gradient Starvation: A Learning Proclivity in Neural Networks. - Mohammad Pezeshki; Sékou-Oumar Kaba; Yoshua Bengio; Aaron C. Courville; Doina Precup; Guillaume Lajoie (NeurIPS, 2021)

Pretraining Representations for Data-Efficient Reinforcement Learning. - Max Schwarzer; Nitarshan Rajkumar; Michael Noukhovitch; Ankesh Anand; Laurent Charlin; R. Devon Hjelm; Philip Bachman; Aaron C. Courville (NeurIPS, 2021)

Touch-based Curiosity for Sparse-Reward Tasks. - Sai Rajeswar; Cyril Ibrahim; Nitin Surya; Florian Golemo; David Vázquez 001; Aaron C. Courville; Pedro O. Pinheiro (CoRR, 2021)

Hierarchical Video Generation for Complex Data. - Lluís Castrejón; Nicolas Ballas; Aaron C. Courville (CoRR, 2021)

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond. - Dinghuai Zhang; Jie Fu 001; Yoshua Bengio; Aaron C. Courville (CoRR, 2021)

Unsupervised Dependency Graph Network. - Yikang Shen; Shawn Tan; Alessandro Sordoni; Peng Li; Jie Zhou; Aaron C. Courville (ACL (1), 2022)

On the Compositional Generalization Gap of In-Context Learning. - Arian Hosseini; Ankit Vani; Dzmitry Bahdanau; Alessandro Sordoni; Aaron C. Courville (BlackboxNLP@EMNLP, 2022)

Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation. - Sai Rajeswar; Issam Hadj Laradji; Pau Rodríguez; David Vázquez 001; Aaron C. Courville (BMVC, 2022)

VIM: Variational Independent Modules for Video Prediction. - Rim Assouel; Lluís Castrejón; Aaron C. Courville; Nicolas Ballas; Yoshua Bengio (CLeaR, 2022)

Multi-label Iterated Learning for Image Classification with Label Ambiguity. - Sai Rajeswar; Pau Rodríguez; Soumye Singhal; David Vázquez 001; Aaron C. Courville (CVPR, 2022)

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. - Aviral Kumar; Rishabh Agarwal; Tengyu Ma 001; Aaron C. Courville; George Tucker; Sergey Levine (ICLR, 2022)

Chunked Autoregressive GAN for Conditional Waveform Synthesis. - Max Morrison; Rithesh Kumar; Kundan Kumar; Prem Seetharaman; Aaron C. Courville; Yoshua Bengio (ICLR, 2022)

Learning to Dequantise with Truncated Flows. - Shawn Tan; Chin-Wei Huang; Alessandro Sordoni; Aaron C. Courville (ICLR, 2022)

MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling. - Yusong Wu; Ethan Manilow; Yi Deng; Rigel Swavely; Kyle Kastner; Tim Cooijmans; Aaron C. Courville; Cheng-Zhi Anna Huang; Jesse H. Engel (ICLR, 2022)

Unifying Likelihood-free Inference with Black-box Optimization and Beyond. - Dinghuai Zhang; Jie Fu 001; Yoshua Bengio; Aaron C. Courville (ICLR, 2022)

Fortuitous Forgetting in Connectionist Networks. - Hattie Zhou; Ankit Vani; Hugo Larochelle; Aaron C. Courville (ICLR, 2022)

The Primacy Bias in Deep Reinforcement Learning. - Evgenii Nikishin; Max Schwarzer; Pierluca D'Oro; Pierre-Luc Bacon; Aaron C. Courville (ICML, 2022)

Generative Flow Networks for Discrete Probabilistic Modeling. - Dinghuai Zhang; Nikolay Malkin; Zhen Liu 019; Alexandra Volokhova; Aaron C. Courville; Yoshua Bengio (ICML, 2022)

Building Robust Ensembles via Margin Boosting. - Dinghuai Zhang; Hongyang Zhang 001; Aaron C. Courville; Yoshua Bengio; Pradeep Ravikumar; Arun Sai Suggala (ICML, 2022)

Cascaded Video Generation for Videos In-the-Wild. - Lluís Castrejón; Nicolas Ballas; Aaron C. Courville (ICPR, 2022)

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2022)

Riemannian Diffusion Models. - Chin-Wei Huang; Milad Aghajohari; Joey Bose; Prakash Panangaden; Aaron C. Courville (NeurIPS, 2022)

Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging. - Taoli Cheng; Aaron C. Courville (CoRR, 2022)

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. - Samuel Lavoie; Christos Tsirigotis; Max Schwarzer; Kenji Kawaguchi; Ankit Vani; Aaron C. Courville (CoRR, 2022)

Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning. - Yuchen Lu; Zhen Liu 019; Aristide Baratin; Romain Laroche; Aaron C. Courville; Alessandro Sordoni (CoRR, 2022)

Beyond Tabula Rasa: Reincarnating Reinforcement Learning. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (CoRR, 2022)

R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS. - Kyle Kastner; Aaron C. Courville (CoRR, 2022)

Unsupervised Model-based Pre-training for Data-efficient Control from Pixels. - Sai Rajeswar; Pietro Mazzaglia; Tim Verbelen; Alexandre Piché; Bart Dhoedt; Aaron C. Courville; Alexandre Lacoste (CoRR, 2022)

Teaching Algorithmic Reasoning via In-context Learning. - Hattie Zhou; Azade Nova; Hugo Larochelle; Aaron C. Courville; Behnam Neyshabur; Hanie Sedghi (CoRR, 2022)

Sparse Universal Transformer. - Shawn Tan; Yikang Shen; Zhenfang Chen; Aaron C. Courville; Chuang Gan 001 (EMNLP, 2023)

SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders. - Kyle Kastner; Tim Cooijmans; Yusong Wu; Aaron C. Courville (EvoMUSART@EvoStar, 2023)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. - Pierluca D'Oro; Max Schwarzer; Evgenii Nikishin; Pierre-Luc Bacon; Marc G. Bellemare; Aaron C. Courville (ICLR, 2023)

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. - Samuel Lavoie; Christos Tsirigotis; Max Schwarzer; Ankit Vani; Michael Noukhovitch; Kenji Kawaguchi; Aaron C. Courville (ICLR, 2023)

Generative Augmented Flow Networks. - Ling Pan; Dinghuai Zhang; Aaron C. Courville; Longbo Huang; Yoshua Bengio (ICLR, 2023)

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. - Adrien Ali Taïga; Rishabh Agarwal; Jesse Farebrother; Aaron C. Courville; Marc G. Bellemare (ICLR, 2023)

Latent State Marginalization as a Low-cost Approach for Improving Exploration. - Dinghuai Zhang; Aaron C. Courville; Yoshua Bengio; Qinqing Zheng; Amy Zhang 001; Ricky T. Q. Chen (ICLR, 2023)

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. - Sai Rajeswar; Pietro Mazzaglia; Tim Verbelen; Alexandre Piché; Bart Dhoedt; Aaron C. Courville; Alexandre Lacoste (ICML, 2023)

Bigger, Better, Faster: Human-level Atari with human-level efficiency. - Max Schwarzer; Johan S. Obando-Ceron; Aaron C. Courville; Marc G. Bellemare; Rishabh Agarwal; Pablo Samuel Castro (ICML, 2023)

Versatile Energy-Based Probabilistic Models for High Energy Physics. - Taoli Cheng; Aaron C. Courville (NeurIPS, 2023)

Double Gumbel Q-Learning. - David Yu-Tung Hui; Aaron C. Courville; Pierre-Luc Bacon (NeurIPS, 2023)

Language Model Alignment with Elastic Reset. - Michael Noukhovitch; Samuel Lavoie; Florian Strub; Aaron C. Courville (NeurIPS, 2023)

Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings. - Yi Ren; Samuel Lavoie; Michael Galkin; Danica J. Sutherland; Aaron C. Courville (NeurIPS, 2023)

Group Robust Classification Without Any Group Information. - Christos Tsirigotis; João Monteiro 002; Pau Rodríguez; David Vázquez 001; Aaron C. Courville (NeurIPS, 2023)

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. - Dinghuai Zhang; Hanjun Dai; Nikolay Malkin; Aaron C. Courville; Yoshua Bengio; Ling Pan (NeurIPS, 2023)

Versatile Energy-Based Models for High Energy Physics. - Taoli Cheng; Aaron C. Courville (CoRR, 2023)

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. - Dinghuai Zhang; Hanjun Dai; Nikolay Malkin; Aaron C. Courville; Yoshua Bengio; Ling Pan (CoRR, 2023)

Meta-Value Learning: a General Framework for Learning with Learning Awareness. - Tim Cooijmans; Milad Aghajohari; Aaron C. Courville (CoRR, 2023)

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. - Max Schwarzer; Jesse Farebrother; Joshua Greaves; Ekin Dogus Cubuk; Rishabh Agarwal; Aaron C. Courville; Marc G. Bellemare; Sergei V. Kalinin; Igor Mordatch; Pablo Samuel Castro; Kevin M. Roccapriore (CoRR, 2023)

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods. - Yuchen Lu; Zhen Liu 019; Aristide Baratin; Romain Laroche; Aaron C. Courville; Alessandro Sordoni (Trans. Mach. Learn. Res., 2023)

SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning. - Bac Nguyen; Stefan Uhlich; Fabien Cardinaux; Lukas Mauch; Marzieh Edraki; Aaron C. Courville (ECCV (69), 2024)

SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision. - Ankit Vani; Bac Nguyen; Samuel Lavoie; Ranjay Krishna; Aaron C. Courville (ECCV (66), 2024)

LOQA: Learning with Opponent Q-Learning Awareness. - Milad Aghajohari; Juan Agustin Duque; Tim Cooijmans; Aaron C. Courville (ICLR, 2024)

The Curse of Diversity in Ensemble-Based Exploration. - Zhixuan Lin; Pierluca D'Oro; Evgenii Nikishin; Aaron C. Courville (ICLR, 2024)

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. - Dinghuai Zhang; Ricky T. Q. Chen; Cheng-Hao Liu; Aaron C. Courville; Yoshua Bengio (ICLR, 2024)

Modeling Caption Diversity in Contrastive Vision-Language Pretraining. - Samuel Lavoie; Polina Kirichenko; Mark Ibrahim; Mido Assran; Andrew Gordon Wilson; Aaron C. Courville; Nicolas Ballas (ICML, 2024)

In value-based deep reinforcement learning, a pruned network is a good network. - Johan S. Obando-Ceron; Aaron C. Courville; Pablo Samuel Castro (ICML, 2024)

Adaptive Accompaniment with ReaLchords. - Yusong Wu; Tim Cooijmans; Kyle Kastner; Adam Roberts; Ian Simon; Alexander Scarlatos; Chris Donahue; Cassie Tarakajian; Shayegan Omidshafiei; Aaron C. Courville; Pablo Samuel Castro; Natasha Jaques; et al. (ICML, 2024)

GenRL: Multimodal-foundation world models for generalization in embodied agents. - Pietro Mazzaglia; Tim Verbelen; Bart Dhoedt; Aaron C. Courville; Sai Rajeswar Mudumba (NeurIPS, 2024)

Best Response Shaping. - Milad Aghajohari; Tim Cooijmans; Juan Agustin Duque; Shunichi Akatsuka; Aaron C. Courville (RLJ, 2024)

On the consistency of hyper-parameter selection in value-based deep reinforcement learning. - Johan S. Obando-Ceron; João Guilherme Madeira Araújo; Aaron C. Courville; Pablo Samuel Castro (RLJ, 2024)

V-STaR: Training Verifiers for Self-Taught Reasoners. - Arian Hosseini; Xingdi Yuan; Nikolay Malkin; Aaron C. Courville; Alessandro Sordoni; Rishabh Agarwal (CoRR, 2024)

In deep reinforcement learning, a pruned network is a good network. - Johan S. Obando-Ceron; Aaron C. Courville; Pablo Samuel Castro (CoRR, 2024)

Scattered Mixture-of-Experts Implementation. - Shawn Tan; Yikang Shen; Rameswar Panda; Aaron C. Courville (CoRR, 2024)

Advantage Alignment Algorithms. - Juan Agustin Duque; Milad Aghajohari; Tim Cooijmans; Tianyu Zhang; Aaron C. Courville (CoRR, 2024)

Multimodal foundation world models for generalist embodied agents. - Pietro Mazzaglia; Tim Verbelen; Bart Dhoedt; Aaron C. Courville; Sai Rajeswar (CoRR, 2024)

Managing multiple agents by automatically adjusting incentives. - Shunichi Akatsuka; Yaemi Teramoto; Aaron C. Courville (CoRR, 2024)

VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment. - Amirhossein Kazemnejad; Milad Aghajohari; Eva Portelance; Alessandro Sordoni; Siva Reddy; Aaron C. Courville; Nicolas Le Roux (CoRR, 2024)

Not All LLM Reasoners Are Created Equal. - Arian Hosseini; Alessandro Sordoni; Daniel Toyama; Aaron C. Courville; Rishabh Agarwal (CoRR, 2024)

Stick-breaking Attention. - Shawn Tan; Yikang Shen; Songlin Yang; Aaron C. Courville; Rameswar Panda (CoRR, 2024)

Distributional GFlowNets with Quantile Flows. - Dinghuai Zhang; Ling Pan; Ricky T. Q. Chen; Aaron C. Courville; Yoshua Bengio (Trans. Mach. Learn. Res., 2024)

Advantage Alignment Algorithms. - Juan Agustin Duque; Milad Aghajohari; Tim Cooijmans; Razvan Ciuca; Tianyu Zhang; Gauthier Gidel; Aaron C. Courville (ICLR, 2025)

Forgetting Transformer: Softmax Attention with a Forget Gate. - Zhixuan Lin; Evgenii Nikishin; Xu Owen He; Aaron C. Courville (ICLR, 2025)

Neuroplastic Expansion in Deep Reinforcement Learning. - Jiashun Liu; Johan S. Obando-Ceron; Aaron C. Courville; Ling Pan (ICLR, 2025)

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models. - Michael Noukhovitch; Shengyi Huang; Sophie Xhonneux; Arian Hosseini; Rishabh Agarwal; Aaron C. Courville (ICLR, 2025)

Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL. - Ghada Sokar; Johan S. Obando-Ceron; Aaron C. Courville; Hugo Larochelle; Pablo Samuel Castro (ICLR, 2025)

Scaling Stick-Breaking Attention: An Efficient Implementation and In-depth Study. - Shawn Tan; Songlin Yang; Aaron C. Courville; Rameswar Panda; Yikang Shen (ICLR, 2025)

Adaptive Computation Pruning for the Forgetting Transformer. - Zhixuan Lin; Johan S. Obando-Ceron; Xu Owen He; Aaron C. Courville (CoRR, 2025)

FLAM: Frame-Wise Language-Audio Modeling. - Yusong Wu; Christos Tsirigotis; Ke Chen; Cheng-Zhi Anna Huang; Aaron C. Courville; Oriol Nieto; Prem Seetharaman; Justin Salamon (CoRR, 2025)

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning. - Jiashun Liu; Zihao Wu; Johan S. Obando-Ceron; Pablo Samuel Castro; Aaron C. Courville; Ling Pan (CoRR, 2025)

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn. - Hongyao Tang; Johan S. Obando-Ceron; Pablo Samuel Castro; Aaron C. Courville; Glen Berseth (CoRR, 2025)

Sample, Predict, then Proceed: Self-Verification Sampling for Tool Use of LLMs. - Shangmin Guo; Omar Darwiche Domingues; Raphaël Avalos; Aaron C. Courville; Florian Strub (CoRR, 2025)

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks. - Walter Mayor; Johan S. Obando-Ceron; Aaron C. Courville; Pablo Samuel Castro (CoRR, 2025)

Bias Analysis in Unconditional Image Generative Models. - Xiaofeng Zhang; Michelle P. Lin; Simon Lacoste-Julien; Aaron C. Courville; Yash Goyal (CoRR, 2025)

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning. - Jiashun Liu; Johan S. Obando-Ceron; Pablo Samuel Castro; Aaron C. Courville; Ling Pan (CoRR, 2025)

Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning. - Roger Creus Castanyer; Johan S. Obando-Ceron; Lu Li; Pierre-Luc Bacon; Glen Berseth; Aaron C. Courville; Pablo Samuel Castro (CoRR, 2025)

Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models. - Samuel Lavoie; Michael Noukhovitch; Aaron C. Courville (CoRR, 2025)

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation. - Christos Tsirigotis; Vaibhav Adlakha; João Monteiro 002; Aaron C. Courville; Perouz Taslakian (CoRR, 2025)