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ORCIDAaron C. Courville
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- affiliation: Université de Montréal, Department of Computer Science
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2020 – today
- 2023
[c135]Kyle Kastner, Tim Cooijmans, Yusong Wu, Aaron C. Courville:
SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders. EvoMUSART@EvoStar 2023: 148-163- [i158]Taoli Cheng, Aaron C. Courville:
Versatile Energy-Based Models for High Energy Physics. CoRR abs/2302.00695 (2023) - [i157]Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio:
Distributional GFlowNets with Quantile Flows. CoRR abs/2302.05793 (2023) - 2022
- [c134]Yikang Shen, Shawn Tan, Alessandro Sordoni, Peng Li, Jie Zhou, Aaron C. Courville:
Unsupervised Dependency Graph Network. ACL (1) 2022: 4767-4784 - [c133]Arian Hosseini, Ankit Vani, Dzmitry Bahdanau, Alessandro Sordoni, Aaron C. Courville:
On the Compositional Generalization Gap of In-Context Learning. BlackboxNLP@EMNLP 2022: 272-280 - [c132]Sai Rajeswar, Issam Hadj Laradji, Pau Rodríguez, David Vázquez, Aaron C. Courville:
Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation. BMVC 2022: 644 - [c131]Rim Assouel, Lluís Castrejón, Aaron C. Courville, Nicolas Ballas, Yoshua Bengio:
VIM: Variational Independent Modules for Video Prediction. CLeaR 2022: 70-89 - [c130]Sai Rajeswar, Pau Rodríguez, Soumye Singhal, David Vázquez, Aaron C. Courville:
Multi-label Iterated Learning for Image Classification with Label Ambiguity. CVPR 2022: 4773-4783 - [c129]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR 2022 - [c128]Max Morrison, Rithesh Kumar, Kundan Kumar, Prem Seetharaman, Aaron C. Courville, Yoshua Bengio:
Chunked Autoregressive GAN for Conditional Waveform Synthesis. ICLR 2022 - [c127]Shawn Tan, Chin-Wei Huang, Alessandro Sordoni, Aaron C. Courville:
Learning to Dequantise with Truncated Flows. ICLR 2022 - [c126]Yusong Wu, Ethan Manilow, Yi Deng, Rigel Swavely, Kyle Kastner, Tim Cooijmans, Aaron C. Courville, Cheng-Zhi Anna Huang, Jesse H. Engel:
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling. ICLR 2022 - [c125]Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville:
Unifying Likelihood-free Inference with Black-box Optimization and Beyond. ICLR 2022 - [c124]Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron C. Courville:
Fortuitous Forgetting in Connectionist Networks. ICLR 2022 - [c123]Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville:
The Primacy Bias in Deep Reinforcement Learning. ICML 2022: 16828-16847 - [c122]Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio:
Generative Flow Networks for Discrete Probabilistic Modeling. ICML 2022: 26412-26428 - [c121]Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala:
Building Robust Ensembles via Margin Boosting. ICML 2022: 26669-26692 - [c120]Lluís Castrejón, Nicolas Ballas, Aaron C. Courville:
Cascaded Video Generation for Videos In-the-Wild. ICPR 2022: 2385-2392 - [c119]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS 2022 - [c118]Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, Aaron C. Courville:
Riemannian Diffusion Models. NeurIPS 2022 - [i156]Taoli Cheng, Aaron C. Courville:
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging. CoRR abs/2201.07199 (2022) - [i155]Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron C. Courville:
Fortuitous Forgetting in Connectionist Networks. CoRR abs/2202.00155 (2022) - [i154]Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio:
Generative Flow Networks for Discrete Probabilistic Modeling. CoRR abs/2202.01361 (2022) - [i153]Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Kenji Kawaguchi, Ankit Vani, Aaron C. Courville:
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. CoRR abs/2204.00616 (2022) - [i152]Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville:
The Primacy Bias in Deep Reinforcement Learning. CoRR abs/2205.07802 (2022) - [i151]Lluís Castrejón, Nicolas Ballas, Aaron C. Courville:
Cascaded Video Generation for Videos In-the-Wild. CoRR abs/2206.00735 (2022) - [i150]Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron C. Courville, Alessandro Sordoni:
Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning. CoRR abs/2206.01251 (2022) - [i149]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR abs/2206.01626 (2022) - [i148]Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala:
Building Robust Ensembles via Margin Boosting. CoRR abs/2206.03362 (2022) - [i147]Kyle Kastner, Aaron C. Courville:
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS. CoRR abs/2206.15276 (2022) - [i146]Chin-Wei Huang, Milad Aghajohari, Avishek Joey Bose, Prakash Panangaden, Aaron C. Courville:
Riemannian Diffusion Models. CoRR abs/2208.07949 (2022) - [i145]Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron C. Courville, Alexandre Lacoste:
Unsupervised Model-based Pre-training for Data-efficient Control from Pixels. CoRR abs/2209.12016 (2022) - [i144]Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen:
Latent State Marginalization as a Low-cost Approach for Improving Exploration. CoRR abs/2210.00999 (2022) - [i143]Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio:
Generative Augmented Flow Networks. CoRR abs/2210.03308 (2022) - [i142]Arian Hosseini, Ankit Vani, Dzmitry Bahdanau, Alessandro Sordoni, Aaron C. Courville:
On the Compositional Generalization Gap of In-Context Learning. CoRR abs/2211.08473 (2022) - [i141]Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi:
Teaching Algorithmic Reasoning via In-context Learning. CoRR abs/2211.09066 (2022) - 2021
- [c117]Yikang Shen, Yi Tay, Che Zheng, Dara Bahri, Donald Metzler, Aaron C. Courville:
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling. ACL/IJCNLP (1) 2021: 7196-7209 - [c116]Michael Noukhovitch, Travis LaCroix, Angeliki Lazaridou, Aaron C. Courville:
Emergent Communication under Competition. AAMAS 2021: 974-982 - [c115]Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vázquez, Aaron C. Courville, Pedro O. Pinheiro:
Haptics-based Curiosity for Sparse-reward Tasks. CoRL 2021: 395-405 - [c114]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Generative Compositional Augmentations for Scene Graph Prediction. ICCV 2021: 15807-15817 - [c113]Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron C. Courville:
Systematic generalisation with group invariant predictions. ICLR 2021 - [c112]Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu:
Neural Approximate Sufficient Statistics for Implicit Models. ICLR 2021 - [c111]Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron C. Courville:
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization. ICLR 2021 - [c110]Samuel Lavoie-Marchildon, Faruk Ahmed, Aaron C. Courville:
Integrating Categorical Semantics into Unsupervised Domain Translation. ICLR 2021 - [c109]Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron C. Courville, Joshua B. Tenenbaum, Chuang Gan:
Learning Task Decomposition with Ordered Memory Policy Network. ICLR 2021 - [c108]Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman:
Data-Efficient Reinforcement Learning with Self-Predictive Representations. ICLR 2021 - [c107]Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron C. Courville:
Iterated learning for emergent systematicity in VQA. ICLR 2021 - [c106]David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville:
Out-of-Distribution Generalization via Risk Extrapolation (REx). ICML 2021: 5815-5826 - [c105]Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron C. Courville, Sarath Chandar:
Continuous Coordination As a Realistic Scenario for Lifelong Learning. ICML 2021: 8016-8024 - [c104]Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville:
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? ICML 2021: 12356-12367 - [c103]Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni, Aaron C. Courville:
Understanding by Understanding Not: Modeling Negation in Language Models. NAACL-HLT 2021: 1301-1312 - [c102]Yikang Shen, Shawn Tan, Alessandro Sordoni, Siva Reddy, Aaron C. Courville:
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle. NAACL-HLT 2021: 1660-1672 - [c101]Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie:
Gradient Starvation: A Learning Proclivity in Neural Networks. NeurIPS 2021: 1256-1272 - [c100]Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville:
Pretraining Representations for Data-Efficient Reinforcement Learning. NeurIPS 2021: 12686-12699 - [c99]Chin-Wei Huang, Jae Hyun Lim, Aaron C. Courville:
A Variational Perspective on Diffusion-Based Generative Models and Score Matching. NeurIPS 2021: 22863-22876 - [c98]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Deep Reinforcement Learning at the Edge of the Statistical Precipice. NeurIPS 2021: 29304-29320 - [i140]Michael Noukhovitch, Travis LaCroix, Angeliki Lazaridou, Aaron C. Courville:
Emergent Communication under Competition. CoRR abs/2101.10276 (2021) - [i139]Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron C. Courville, Sarath Chandar:
Continuous Coordination As a Realistic Scenario for Lifelong Learning. CoRR abs/2103.03216 (2021) - [i138]Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron C. Courville, Joshua B. Tenenbaum, Chuang Gan:
Learning Task Decomposition with Ordered Memory Policy Network. CoRR abs/2103.10972 (2021) - [i137]Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vázquez, Aaron C. Courville, Pedro O. Pinheiro:
Touch-based Curiosity for Sparse-Reward Tasks. CoRR abs/2104.00442 (2021) - [i136]Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron C. Courville:
Iterated learning for emergent systematicity in VQA. CoRR abs/2105.01119 (2021) - [i135]Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni, Aaron C. Courville:
Understanding by Understanding Not: Modeling Negation in Language Models. CoRR abs/2105.03519 (2021) - [i134]Lluís Castrejón, Nicolas Ballas, Aaron C. Courville:
Hierarchical Video Generation for Complex Data. CoRR abs/2106.02719 (2021) - [i133]Chin-Wei Huang, Jae Hyun Lim, Aaron C. Courville:
A Variational Perspective on Diffusion-Based Generative Models and Score Matching. CoRR abs/2106.02808 (2021) - [i132]Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville:
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? CoRR abs/2106.02890 (2021) - [i131]Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville:
Pretraining Representations for Data-Efficient Reinforcement Learning. CoRR abs/2106.04799 (2021) - [i130]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR abs/2108.13264 (2021) - [i129]Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare:
On Bonus-Based Exploration Methods in the Arcade Learning Environment. CoRR abs/2109.11052 (2021) - [i128]Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville:
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond. CoRR abs/2110.03372 (2021) - [i127]Max Morrison, Rithesh Kumar, Kundan Kumar, Prem Seetharaman, Aaron C. Courville, Yoshua Bengio:
Chunked Autoregressive GAN for Conditional Waveform Synthesis. CoRR abs/2110.10139 (2021) - [i126]Sai Rajeswar, Pau Rodríguez, Soumye Singhal, David Vázquez, Aaron C. Courville:
Multi-label Iterated Learning for Image Classification with Label Ambiguity. CoRR abs/2111.12172 (2021) - [i125]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR abs/2112.04716 (2021) - [i124]Yusong Wu, Ethan Manilow, Yi Deng, Rigel Swavely, Kyle Kastner, Tim Cooijmans, Aaron C. Courville, Cheng-Zhi Anna Huang, Jesse H. Engel:
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling. CoRR abs/2112.09312 (2021) - 2020
- [j14]Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio:
Generative adversarial networks. Commun. ACM 63(11): 139-144 (2020) - [j13]Sai Rajeswar, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vázquez, Derek Nowrouzezahrai, Aaron C. Courville:
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation. Int. J. Comput. Vis. 128(10): 2478-2493 (2020) - [c97]Faruk Ahmed, Aaron C. Courville:
Detecting Semantic Anomalies. AAAI 2020: 3154-3162 - [c96]Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Joelle Pineau, Aaron C. Courville, Laurent Charlin, Yoshua Bengio:
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. AIED (2) 2020: 387-392 - [c95]Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron C. Courville:
Stochastic Neural Network with Kronecker Flow. AISTATS 2020: 4184-4194 - [c94]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation. BMVC 2020 - [c93]Shawn Tan, Yikang Shen, Alessandro Sordoni, Aaron C. Courville, Timothy J. O'Donnell:
Recursive Top-Down Production for Sentence Generation with Latent Trees. EMNLP (Findings) 2020: 2291-2307 - [c92]Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron C. Courville:
Supervised Seeded Iterated Learning for Interactive Language Learning. EMNLP (1) 2020: 3962-3970 - [c91]Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare:
On Bonus Based Exploration Methods In The Arcade Learning Environment. ICLR 2020 - [c90]Jae Hyun Lim, Aaron C. Courville, Christopher J. Pal, Chin-Wei Huang:
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation. ICML 2020: 6061-6071 - [c89]Yuchen Lu, Soumye Singhal, Florian Strub, Aaron C. Courville, Olivier Pietquin:
Countering Language Drift with Seeded Iterated Learning. ICML 2020: 6437-6447 - [c88]Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron C. Courville:
Unsupervised Learning of Dense Visual Representations. NeurIPS 2020 - [i123]Chin-Wei Huang, Laurent Dinh, Aaron C. Courville:
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models. CoRR abs/2002.07101 (2020) - [i122]David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville:
Out-of-Distribution Generalization via Risk Extrapolation (REx). CoRR abs/2003.00688 (2020) - [i121]Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron C. Courville:
Countering Language Drift with Seeded Iterated Learning. CoRR abs/2003.12694 (2020) - [i120]Sai Rajeswar, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vázquez, Derek Nowrouzezahrai, Aaron C. Courville:
Pix2Shape - Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation. CoRR abs/2003.14166 (2020) - [i119]Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Joelle Pineau, Aaron C. Courville, Laurent Charlin, Yoshua Bengio:
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. CoRR abs/2005.06616 (2020) - [i118]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation. CoRR abs/2005.08230 (2020) - [i117]Jae Hyun Lim, Aaron C. Courville, Christopher J. Pal, Chin-Wei Huang:
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation. CoRR abs/2006.05164 (2020) - [i116]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Generative Graph Perturbations for Scene Graph Prediction. CoRR abs/2007.05756 (2020) - [i115]Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman:
Data-Efficient Reinforcement Learning with Momentum Predictive Representations. CoRR abs/2007.05929 (2020) - [i114]Samuel Lavoie-Marchildon, Faruk Ahmed, Aaron C. Courville:
Integrating Categorical Semantics into Unsupervised Domain Translation. CoRR abs/2010.01262 (2020) - [i113]Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron C. Courville:
Supervised Seeded Iterated Learning for Interactive Language Learning. CoRR abs/2010.02975 (2020) - [i112]Shawn Tan, Yikang Shen, Timothy J. O'Donnell, Alessandro Sordoni, Aaron C. Courville:
Recursive Top-Down Production for Sentence Generation with Latent Trees. CoRR abs/2010.04704 (2020) - [i111]Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu:
Neural Approximate Sufficient Statistics for Implicit Models. CoRR abs/2010.10079 (2020) - [i110]Rithesh Kumar, Kundan Kumar, Vicki Anand, Yoshua Bengio, Aaron C. Courville:
NU-GAN: High resolution neural upsampling with GAN. CoRR abs/2010.11362 (2020) - [i109]Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron C. Courville:
Unsupervised Learning of Dense Visual Representations. CoRR abs/2011.05499 (2020) - [i108]Yikang Shen, Shawn Tan, Alessandro Sordoni, Siva Reddy, Aaron C. Courville:
Explicitly Modeling Syntax in Language Model improves Generalization. CoRR abs/2011.07960 (2020) - [i107]Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie:
Gradient Starvation: A Learning Proclivity in Neural Networks. CoRR abs/2011.09468 (2020) - [i106]Yikang Shen, Yi Tay, Che Zheng, Dara Bahri, Donald Metzler, Aaron C. Courville:
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling. CoRR abs/2012.00857 (2020) - [i105]Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron C. Courville:
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization. CoRR abs/2012.05942 (2020)
2010 – 2019
- 2019
- [c87]Catalina Cangea, Eugene Belilovsky, Aaron C. Courville:
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. BMVC 2019: 280 - [c86]Kyle Kastner, João Felipe Santos, Yoshua Bengio, Aaron C. Courville:
Representation Mixing for TTS Synthesis. ICASSP 2019: 5906-5910 - [c85]Mikolaj Binkowski, R. Devon Hjelm, Aaron C. Courville:
Batch Weight for Domain Adaptation With Mass Shift. ICCV 2019: 1844-1853 - [c84]Lluís Castrejón, Nicolas Ballas, Aaron C. Courville:
Improved Conditional VRNNs for Video Prediction. ICCV 2019: 7607-7616 - [c83]Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron C. Courville:
Systematic Generalization: What Is Required and Can It Be Learned? ICLR (Poster) 2019 - [c82]Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron C. Courville:
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. ICLR 2019 - [c81]Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron C. Courville:
Hierarchical Importance Weighted Autoencoders. ICML 2019: 2869-2878 - [c80]Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville:
On the Spectral Bias of Neural Networks. ICML 2019: 5301-5310 - [c79]Lucas Caccia, Herke van Hoof, Aaron C. Courville, Joelle Pineau:
Deep Generative Modeling of LiDAR Data. IROS 2019: 5034-5040 - [c78]Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron C. Courville:
No-Press Diplomacy: Modeling Multi-Agent Gameplay. NeurIPS 2019: 4476-4487 - [c77]Yikang Shen, Shawn Tan, Seyed Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron C. Courville:
Ordered Memory. NeurIPS 2019: 5038-5049 - [c76]Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brébisson, Yoshua Bengio, Aaron C. Courville:
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis. NeurIPS 2019: 14881-14892 - [c75]Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron C. Courville:
Probability Distillation: A Caveat and Alternatives. UAI 2019: 1212-1221 - [i104]Catalina Cangea, Eugene Belilovsky, Pietro Liò, Aaron C. Courville:
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. ViGIL@NeurIPS 2019 - [i103]Harm de Vries, Dzmitry Bahdanau, Shikhar Murty, Aaron C. Courville, Philippe Beaudoin:
CLOSURE: Assessing Systematic Generalization of CLEVR Models. ViGIL@NeurIPS 2019 - [i102]Rithesh Kumar, Anirudh Goyal, Aaron C. Courville, Yoshua Bengio:
Maximum Entropy Generators for Energy-Based Models. CoRR abs/1901.08508 (2019) - [i101]