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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
- 2024
[j16]Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio:
Distributional GFlowNets with Quantile Flows. Trans. Mach. Learn. Res. 2024 (2024)- [c155]Milad Aghajohari, Juan Agustin Duque, Tim Cooijmans, Aaron C. Courville:
LOQA: Learning with Opponent Q-Learning Awareness. ICLR 2024 - [c154]Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron C. Courville:
The Curse of Diversity in Ensemble-Based Exploration. ICLR 2024 - [c153]Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio:
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. ICLR 2024 - [c152]Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mido Assran, Andrew Gordon Wilson, Aaron C. Courville, Nicolas Ballas:
Modeling Caption Diversity in Contrastive Vision-Language Pretraining. ICML 2024 - [c151]Johan Samir Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro:
In value-based deep reinforcement learning, a pruned network is a good network. ICML 2024 - [c150]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, Cheng-Zhi Anna Huang:
Adaptive Accompaniment with ReaLchords. ICML 2024 - [i179]Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron C. Courville, Alessandro Sordoni, Rishabh Agarwal:
V-STaR: Training Verifiers for Self-Taught Reasoners. CoRR abs/2402.06457 (2024) - [i178]Johan S. Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro:
In deep reinforcement learning, a pruned network is a good network. CoRR abs/2402.12479 (2024) - [i177]Shawn Tan, Yikang Shen, Rameswar Panda, Aaron C. Courville:
Scattered Mixture-of-Experts Implementation. CoRR abs/2403.08245 (2024) - [i176]Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron C. Courville:
Best Response Shaping. CoRR abs/2404.06519 (2024) - [i175]Ankit Vani, Bac Nguyen, Samuel Lavoie, Ranjay Krishna, Aaron C. Courville:
SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision. CoRR abs/2404.15721 (2024) - [i174]Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mahmoud Assran, Andrew Gordon Wilson, Aaron C. Courville, Nicolas Ballas:
Modeling Caption Diversity in Contrastive Vision-Language Pretraining. CoRR abs/2405.00740 (2024) - [i173]Milad Aghajohari, Juan Agustin Duque, Tim Cooijmans, Aaron C. Courville:
LOQA: Learning with Opponent Q-Learning Awareness. CoRR abs/2405.01035 (2024) - [i172]Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron C. Courville:
The Curse of Diversity in Ensemble-Based Exploration. CoRR abs/2405.04342 (2024) - [i171]Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, Tianyu Zhang, Aaron C. Courville:
Advantage Alignment Algorithms. CoRR abs/2406.14662 (2024) - [i170]Johan S. Obando-Ceron, João G. M. Araújo, Aaron C. Courville, Pablo Samuel Castro:
On the consistency of hyper-parameter selection in value-based deep reinforcement learning. CoRR abs/2406.17523 (2024) - [i169]Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron C. Courville, Sai Rajeswar:
Multimodal foundation world models for generalist embodied agents. CoRR abs/2406.18043 (2024) - [i168]Bac Nguyen, Stefan Uhlich, Fabien Cardinaux, Lukas Mauch, Marzieh Edraki, Aaron C. Courville:
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning. CoRR abs/2407.03036 (2024) - 2023
- [j15]Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron C. Courville, Alessandro Sordoni:
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods. Trans. Mach. Learn. Res. 2023 (2023) - [c149]Shawn Tan, Yikang Shen, Zhenfang Chen, Aaron C. Courville, Chuang Gan:
Sparse Universal Transformer. EMNLP 2023: 169-179 - [c148]Kyle Kastner, Tim Cooijmans, Yusong Wu, Aaron C. Courville:
SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders. EvoMUSART@EvoStar 2023: 148-163 - [c147]Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville:
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. ICLR 2023 - [c146]Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi, Aaron C. Courville:
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. ICLR 2023 - [c145]Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio:
Generative Augmented Flow Networks. ICLR 2023 - [c144]Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare:
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. ICLR 2023 - [c143]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. ICLR 2023 - [c142]Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron C. Courville, Alexandre Lacoste:
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. ICML 2023: 28598-28617 - [c141]Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro:
Bigger, Better, Faster: Human-level Atari with human-level efficiency. ICML 2023: 30365-30380 - [c140]Taoli Cheng, Aaron C. Courville:
Versatile Energy-Based Probabilistic Models for High Energy Physics. NeurIPS 2023 - [c139]David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon:
Double Gumbel Q-Learning. NeurIPS 2023 - [c138]Michael Noukhovitch, Samuel Lavoie, Florian Strub, Aaron C. Courville:
Language Model Alignment with Elastic Reset. NeurIPS 2023 - [c137]Yi Ren, Samuel Lavoie, Michael Galkin, Danica J. Sutherland, Aaron C. Courville:
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings. NeurIPS 2023 - [c136]Christos Tsirigotis, João Monteiro, Pau Rodríguez, David Vázquez, Aaron C. Courville:
Group Robust Classification Without Any Group Information. NeurIPS 2023 - [c135]Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan:
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. NeurIPS 2023 - [i167]Taoli Cheng, Aaron C. Courville:
Versatile Energy-Based Models for High Energy Physics. CoRR abs/2302.00695 (2023) - [i166]Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio:
Distributional GFlowNets with Quantile Flows. CoRR abs/2302.05793 (2023) - [i165]Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan:
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. CoRR abs/2305.17010 (2023) - [i164]Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro:
Bigger, Better, Faster: Human-level Atari with human-level efficiency. CoRR abs/2305.19452 (2023) - [i163]Tim Cooijmans, Milad Aghajohari, Aaron C. Courville:
Meta-Value Learning: a General Framework for Learning with Learning Awareness. CoRR abs/2307.08863 (2023) - [i162]Dinghuai Zhang, Ricky Tian Qi Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio:
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. CoRR abs/2310.02679 (2023) - [i161]Shawn Tan, Yikang Shen, Zhenfang Chen, Aaron C. Courville, Chuang Gan:
Sparse Universal Transformer. CoRR abs/2310.07096 (2023) - [i160]Christos Tsirigotis, João Monteiro, Pau Rodríguez, David Vázquez, Aaron C. Courville:
Group Robust Classification Without Any Group Information. CoRR abs/2310.18555 (2023) - [i159]Yi Ren, Samuel Lavoie, Mikhail Galkin, Danica J. Sutherland, Aaron C. Courville:
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings. CoRR abs/2310.18777 (2023) - [i158]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:
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. CoRR abs/2311.17894 (2023) - [i157]Michael Noukhovitch, Samuel Lavoie, Florian Strub, Aaron C. Courville:
Language Model Alignment with Elastic Reset. CoRR abs/2312.07551 (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]