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ORCIDAaron C. Courville
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- affiliation: Université de Montréal, Department of Computer Science
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Books and Theses
- 2016
[b1]Ian J. Goodfellow, Yoshua Bengio, Aaron C. Courville:
Deep Learning. Adaptive computation and machine learning, MIT Press 2016, ISBN 978-0-262-03561-3, pp. 1-775
Journal Articles
- 2024
- [j18]Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron C. Courville:
Best Response Shaping. RLJ 2: 798-818 (2024) - [j17]Johan Samir Obando-Ceron, João Guilherme Madeira Araújo, Aaron C. Courville, Pablo Samuel Castro:
On the consistency of hyper-parameter selection in value-based deep reinforcement learning. RLJ 3: 1037-1059 (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) - 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) - 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) - 2017
- [j12]Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Joseph Pal, Hugo Larochelle, Aaron C. Courville, Bernt Schiele
:
Movie Description. Int. J. Comput. Vis. 123(1): 94-120 (2017) - [j11]Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron C. Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle:
Brain tumor segmentation with Deep Neural Networks. Medical Image Anal. 35: 18-31 (2017) - 2016
- [j10]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, David Warde-Farley, Aaron C. Courville, Pascal Vincent, Roland Memisevic, Christopher Joseph Pal, Yoshua Bengio:
EmoNets: Multimodal deep learning approaches for emotion recognition in video. J. Multimodal User Interfaces 10(2): 99-111 (2016) - 2015
- [j9]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, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio:
Challenges in representation learning: A report on three machine learning contests. Neural Networks 64: 59-63 (2015) - [j8]Kyunghyun Cho, Aaron C. Courville, Yoshua Bengio:
Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks. IEEE Trans. Multim. 17(11): 1875-1886 (2015) - 2014
- [j7]Aaron C. Courville, Guillaume Desjardins, James Bergstra, Yoshua Bengio:
The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions. IEEE Trans. Pattern Anal. Mach. Intell. 36(9): 1874-1887 (2014) - 2013
- [j6]Yoshua Bengio, Aaron C. Courville, Pascal Vincent:
Representation Learning: A Review and New Perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8): 1798-1828 (2013) - [j5]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning. IEEE Trans. Pattern Anal. Mach. Intell. 35(8): 1902-1914 (2013) - 2011
- [j4]Robert D. Vincent, Aaron C. Courville, Joelle Pineau:
A bistable computational model of recurring epileptiform activity as observed in rodent slice preparations. Neural Networks 24(6): 526-537 (2011) - 2010
- [j3]Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio:
Why Does Unsupervised Pre-training Help Deep Learning? J. Mach. Learn. Res. 11: 625-660 (2010) - 2006
- [j2]Carl Wellington, Aaron C. Courville, Anthony Stentz:
A Generative Model of Terrain for Autonomous Navigation in Vegetation. Int. J. Robotics Res. 25(12): 1287-1304 (2006) - [j1]Nathaniel D. Daw, Aaron C. Courville, David S. Touretzky:
Representation and Timing in Theories of the Dopamine System. Neural Comput. 18(7): 1637-1677 (2006)
Conference and Workshop Papers
- 2024
- [c158]Bac Nguyen, Stefan Uhlich, Fabien Cardinaux, Lukas Mauch, Marzieh Edraki, Aaron C. Courville:
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning. ECCV (69) 2024: 138-154 - [c157]Ankit Vani, Bac Nguyen, Samuel Lavoie, Ranjay Krishna, Aaron C. Courville:
SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision. ECCV (66) 2024: 233-251 - [c156]Milad Aghajohari, Juan Agustin Duque, Tim Cooijmans, Aaron C. Courville:
LOQA: Learning with Opponent Q-Learning Awareness. ICLR 2024 - [c155]Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron C. Courville:
The Curse of Diversity in Ensemble-Based Exploration. ICLR 2024 - [c154]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 - [c153]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 - [c152]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 - [c151]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 - [c150]Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron C. Courville, Sai Rajeswar Mudumba:
GenRL: Multimodal-foundation world models for generalization in embodied agents. NeurIPS 2024 - 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 - 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 - 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 - 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 - 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 - 2018
- [c74]Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron C. Courville:
FiLM: Visual Reasoning with a General Conditioning Layer. AAAI 2018: 3942-3951 - [c73]Yikang Shen, Zhouhan Lin, Athul Paul Jacob, Alessandro Sordoni, Aaron C. Courville, Yoshua Bengio:
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance. ACL (1) 2018: 1171-1180 - [c72]Florian Golemo, Adrien Ali Taïga, Aaron C. Courville, Pierre-Yves Oudeyer:
Sim-to-Real Transfer with Neural-Augmented Robot Simulation. CoRL 2018: 817-828 - [c71]Florian Strub, Mathieu Seurin, Ethan Perez, Harm de Vries, Jérémie Mary, Philippe Preux, Aaron C. Courville, Olivier Pietquin:
Visual Reasoning with Multi-hop Feature Modulation. ECCV (5) 2018: 808-831 - [c70]Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron C. Courville:
HoME: a Household Multimodal Environment. ICLR (Workshop) 2018 - [c69]Yikang Shen, Zhouhan Lin, Chin-Wei Huang, Aaron C. Courville:
Neural Language Modeling by Jointly Learning Syntax and Lexicon. ICLR (Poster) 2018 - [c68]Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville:
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. ICML 2018: 195-204 - [c67]Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, Aaron C. Courville:
Mutual Information Neural Estimation. ICML 2018: 530-539 - [c66]Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron C. Courville:
Neural Autoregressive Flows. ICML 2018: 2083-2092 - [c65]Sandeep Subramanian, Sai Rajeswar, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, Chris Pal:
Towards Text Generation with Adversarially Learned Neural Outlines. NeurIPS 2018: 7562-7574 - [c64]Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron C. Courville:
Improving Explorability in Variational Inference with Annealed Variational Objectives. NeurIPS 2018: 9724-9734 - 2017
- [c63]Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron C. Courville:
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation. AAAI 2017: 3288-3294 - [c62]Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C. Courville, Yoshua Bengio:
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. AAAI 2017: 3295-3301 - [c61]Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron C. Courville:
GuessWhat?! Visual Object Discovery through Multi-modal Dialogue. CVPR 2017: 4466-4475 - [c60]Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron C. Courville, Christopher Joseph Pal:
A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering. CVPR 2017: 7359-7368 - [c59]Iulian Vlad Serban, Alexander Ororbia, Joelle Pineau, Aaron C. Courville:
Piecewise Latent Variables for Neural Variational Text Processing. SPNLP@EMNLP 2017: 52-62 - [c58]Iulian Vlad Serban, Alexander G. Ororbia II, Joelle Pineau, Aaron C. Courville:
Piecewise Latent Variables for Neural Variational Text Processing. EMNLP 2017: 422-432 - [c57]Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, Yoshua Bengio:
An Actor-Critic Algorithm for Sequence Prediction. ICLR (Poster) 2017 - [c56]Tim Cooijmans, Nicolas Ballas, César Laurent, Çaglar Gülçehre, Aaron C. Courville:
Recurrent Batch Normalization. ICLR (Poster) 2017 - [c55]Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville:
Calibrating Energy-based Generative Adversarial Networks. ICLR (Poster) 2017 - [c54]Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville:
Adversarially Learned Inference. ICLR (Poster) 2017 - [c53]Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taïga, Francesco Visin, David Vázquez, Aaron C. Courville:
PixelVAE: A Latent Variable Model for Natural Images. ICLR (Poster) 2017 - [c52]David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron C. Courville:
Deep Nets Don't Learn via Memorization. ICLR (Workshop) 2017 - [c51]David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. ICLR (Poster) 2017 - [c50]Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron C. Courville, Yoshua Bengio:
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. ICLR (Poster) 2017 - [c49]Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Generalizable Features From Unsupervised Learning. ICLR (Workshop) 2017 - [c48]Jose Sotelo, Soroush Mehri, Kundan Kumar, João Felipe Santos, Kyle Kastner, Aaron C. Courville, Yoshua Bengio:
Char2Wav: End-to-End Speech Synthesis. ICLR (Workshop) 2017 - [c47]Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien:
A Closer Look at Memorization in Deep Networks. ICML 2017: 233-242 - [c46]Florian Strub, Harm de Vries, Jérémie Mary, Bilal Piot, Aaron C. Courville, Olivier Pietquin:
End-to-end optimization of goal-driven and visually grounded dialogue systems. IJCAI 2017: 2765-2771 - [c45]Cheng-Zhi Anna Huang, Tim Cooijmans, Adam Roberts, Aaron C. Courville, Douglas Eck:
Counterpoint by Convolution. ISMIR 2017: 211-218 - [c44]Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio:
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. NIPS 2017: 5089-5098 - [c43]Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, Aaron C. Courville:
Improved Training of Wasserstein GANs. NIPS 2017: 5767-5777 - [c42]Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C. Courville:
Modulating early visual processing by language. NIPS 2017: 6594-6604 - [c41]Sandeep Subramanian, Sai Rajeswar, Francis Dutil, Chris Pal, Aaron C. Courville:
Adversarial Generation of Natural Language. Rep4NLP@ACL 2017: 241-251 - 2016
- [c40]Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C. Courville, Joelle Pineau:
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. AAAI 2016: 3776-3784 - [c39]Iulian Vlad Serban, Alberto García-Durán, Çaglar Gülçehre, Sungjin Ahn, Sarath Chandar, Aaron C. Courville, Yoshua Bengio:
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus. ACL (1) 2016 - [c38]Francesco Visin, Adriana Romero, Kyunghyun Cho, Matteo Matteucci, Marco Ciccone, Kyle Kastner, Yoshua Bengio, Aaron C. Courville:
ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation. CVPR Workshops 2016: 426-433 - [c37]Harm de Vries, Roland Memisevic, Aaron C. Courville:
Deep Learning Vector Quantization. ESANN 2016 - [c36]Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron C. Courville, Yoshua Bengio:
Deconstructing the Ladder Network Architecture. ICML 2016: 2368-2376 - [c35]Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron C. Courville:
Dynamic Capacity Networks. ICML 2016: 2549-2558 - [c34]Ying Zhang, Mohammad Pezeshki, Philémon Brakel, Saizheng Zhang, César Laurent, Yoshua Bengio, Aaron C. Courville:
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. INTERSPEECH 2016: 410-414 - [c33]Anirudh Goyal, Alex Lamb, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio:
Professor Forcing: A New Algorithm for Training Recurrent Networks. NIPS 2016: 4601-4609 - [c32]Nicolas Ballas, Li Yao, Chris Pal, Aaron C. Courville:
Delving Deeper into Convolutional Networks for Learning Video Representations. ICLR (Poster) 2016 - 2015
- [c31]Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher J. Pal, Hugo Larochelle, Aaron C. Courville:
Describing Videos by Exploiting Temporal Structure. ICCV 2015: 4507-4515 - [c30]Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio:
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. ICML 2015: 2048-2057 - [c29]Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio:
A Recurrent Latent Variable Model for Sequential Data. NIPS 2015: 2980-2988 - [c28]Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron C. Courville:
Learning Distributed Representations from Reviews for Collaborative Filtering. RecSys 2015: 147-154 - 2014
- [c27]Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
On the Challenges of Physical Implementations of RBMs. AAAI 2014: 1199-1205 - [c26]Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio:
Generative Adversarial Nets. NIPS 2014: 2672-2680 - [c25]Ian J. Goodfellow, Mehdi Mirza, Xia Da, Aaron C. Courville, Yoshua Bengio:
An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks. ICLR (Poster) 2014 - [c24]David Warde-Farley, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
An empirical analysis of dropout in piecewise linear networks. ICLR (Poster) 2014 - 2013
- [c23]Heng Luo, Pierre Luc Carrier, Aaron C. Courville, Yoshua Bengio:
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions. AISTATS 2013: 415-423 - [c22]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, Pierre Luc Carrier, Yann N. Dauphin, Nicolas Boulanger-Lewandowski, Abhishek Aggarwal, Jeremie Zumer, Pascal Lamblin, Jean-Philippe Raymond, Guillaume Desjardins, Razvan Pascanu, David Warde-Farley, Atousa Torabi, Arjun Sharma, Emmanuel Bengio, Kishore Reddy Konda, Zhenzhou Wu:
Combining modality specific deep neural networks for emotion recognition in video. ICMI 2013: 543-550 - [c21]Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Maxout Networks. ICML (3) 2013: 1319-1327 - [c20]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, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio:
Challenges in Representation Learning: A Report on Three Machine Learning Contests. ICONIP (3) 2013: 117-124 - [c19]Ian J. Goodfellow, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Multi-Prediction Deep Boltzmann Machines. NIPS 2013: 548-556 - [c18]Guillaume Desjardins, Razvan Pascanu, Aaron C. Courville, Yoshua Bengio:
Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines. ICLR (Poster) 2013 - [c17]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Joint Training Deep Boltzmann Machines for Classification. ICLR (Workshop) 2013 - 2012
- [c16]Salah Rifai, Yoshua Bengio, Aaron C. Courville, Pascal Vincent, Mehdi Mirza:
Disentangling Factors of Variation for Facial Expression Recognition. ECCV (6) 2012: 808-822 - [c15]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding. ICML 2012 - [c14]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, James Bergstra:
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. ICML Unsupervised and Transfer Learning 2012: 97-110 - 2011
- [c13]Aaron C. Courville, James Bergstra, Yoshua Bengio:
Unsupervised Models of Images by Spikeand-Slab RBMs. ICML 2011: 1145-1152 - [c12]Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio:
On Tracking The Partition Function. NIPS 2011: 2501-2509 - [c11]Aaron C. Courville, James Bergstra, Yoshua Bengio:
A Spike and Slab Restricted Boltzmann Machine. AISTATS 2011: 233-241 - 2010
- [c10]Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau:
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines. AISTATS 2010: 145-152 - [c9]Dumitru Erhan, Aaron C. Courville, Yoshua Bengio, Pascal Vincent:
Why Does Unsupervised Pre-training Help Deep Learning? AISTATS 2010: 201-208 - 2009
- [c8]Aaron C. Courville, Douglas Eck, Yoshua Bengio:
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism. NIPS 2009: 405-413 - 2007
- [c7]Hugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio:
An empirical evaluation of deep architectures on problems with many factors of variation. ICML 2007: 473-480 - [c6]Nathaniel D. Daw, Aaron C. Courville:
The rat as particle filter. NIPS 2007: 369-376 - 2005
- [c5]Carl Wellington, Aaron C. Courville, Anthony Stentz:
Interacting Markov Random Fields for Simultaneous Terrain Modeling and Obstacle Detection. Robotics: Science and Systems 2005: 1-8 - 2004
- [c4]Aaron C. Courville, Nathaniel D. Daw, David S. Touretzky:
Similarity and Discrimination in Classical Conditioning: A Latent Variable Account. NIPS 2004: 313-320 - 2003
- [c3]Aaron C. Courville, Nathaniel D. Daw, Geoffrey J. Gordon, David S. Touretzky:
Model Uncertainty in Classical Conditioning. NIPS 2003: 977-984 - 2002
- [c2]Nathaniel D. Daw, Aaron C. Courville, David S. Touretzky:
Timing and Partial Observability in the Dopamine System. NIPS 2002: 83-90 - 2001
- [c1]Aaron C. Courville, David S. Touretzky:
Modeling Temporal Structure in Classical Conditioning. NIPS 2001: 3-10
Parts in Books or Collections
- 2013
- [p2]Ross Messing, Atousa Torabi, Aaron C. Courville:
Evaluating and Extending Trajectory Features for Activity Recognition. Advanced Topics in Computer Vision 2013: 95-111 - [p1]Yoshua Bengio, Aaron C. Courville:
Deep Learning of Representations. Handbook on Neural Information Processing 2013: 1-28
Informal and Other Publications
- 2024
- [i186]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) - [i185]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) - [i184]Shawn Tan, Yikang Shen, Rameswar Panda, Aaron C. Courville:
Scattered Mixture-of-Experts Implementation. CoRR abs/2403.08245 (2024) - [i183]Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron C. Courville:
Best Response Shaping. CoRR abs/2404.06519 (2024) - [i182]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) - [i181]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) - [i180]Milad Aghajohari, Juan Agustin Duque, Tim Cooijmans, Aaron C. Courville:
LOQA: Learning with Opponent Q-Learning Awareness. CoRR abs/2405.01035 (2024) - [i179]Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron C. Courville:
The Curse of Diversity in Ensemble-Based Exploration. CoRR abs/2405.04342 (2024) - [i178]Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, Tianyu Zhang, Aaron C. Courville:
Advantage Alignment Algorithms. CoRR abs/2406.14662 (2024) - [i177]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) - [i176]Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron C. Courville, Sai Rajeswar:
Multimodal foundation world models for generalist embodied agents. CoRR abs/2406.18043 (2024) - [i175]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) - [i174]Shunichi Akatsuka, Yaemi Teramoto, Aaron C. Courville:
Managing multiple agents by automatically adjusting incentives. CoRR abs/2409.02960 (2024) - [i173]Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron C. Courville, Nicolas Le Roux:
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment. CoRR abs/2410.01679 (2024) - [i172]Arian Hosseini, Alessandro Sordoni, Daniel Toyama, Aaron C. Courville, Rishabh Agarwal:
Not All LLM Reasoners Are Created Equal. CoRR abs/2410.01748 (2024) - [i171]Ghada Sokar, Johan S. Obando-Ceron, Aaron C. Courville, Hugo Larochelle, Pablo Samuel Castro:
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL. CoRR abs/2410.01930 (2024) - [i170]Jiashun Liu, Johan S. Obando-Ceron, Aaron C. Courville, Ling Pan:
Neuroplastic Expansion in Deep Reinforcement Learning. CoRR abs/2410.07994 (2024) - [i169]Shawn Tan, Yikang Shen, Songlin Yang, Aaron C. Courville, Rameswar Panda:
Stick-breaking Attention. CoRR abs/2410.17980 (2024) - [i168]Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux, Arian Hosseini, Rishabh Agarwal, Aaron C. Courville:
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models. CoRR abs/2410.18252 (2024) - 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
- [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
- [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
- [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) - 2019
- [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]Cheng-Zhi Anna Huang, Tim Cooijmans, Adam Roberts, Aaron C. Courville, Douglas Eck:
Counterpoint by Convolution. CoRR abs/1903.07227 (2019) - [i100]Lluís Castrejón, Nicolas Ballas, Aaron C. Courville:
Improved Conditional VRNNs for Video Prediction. CoRR abs/1904.12165 (2019) - [i99]Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron C. Courville:
Hierarchical Importance Weighted Autoencoders. CoRR abs/1905.04866 (2019) - [i98]Mikolaj Binkowski, R. Devon Hjelm, Aaron C. Courville:
Batch weight for domain adaptation with mass shift. CoRR abs/1905.12760 (2019) - [i97]Chin-Wei Huang, Aaron C. Courville:
Note on the bias and variance of variational inference. CoRR abs/1906.03708 (2019) - [i96]Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron C. Courville:
Stochastic Neural Network with Kronecker Flow. CoRR abs/1906.04282 (2019) - [i95]Shawn Tan, Yikang Shen, Chin-Wei Huang, Aaron C. Courville:
Investigating Biases in Textual Entailment Datasets. CoRR abs/1906.09635 (2019) - [i94]Jacob Leygonie, Jennifer She, Amjad Almahairi, Sai Rajeswar, Aaron C. Courville:
Adversarial Computation of Optimal Transport Maps. CoRR abs/1906.09691 (2019) - [i93]Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare:
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment. CoRR abs/1908.02388 (2019) - [i92]Faruk Ahmed, Aaron C. Courville:
Detecting semantic anomalies. CoRR abs/1908.04388 (2019) - [i91]Catalina Cangea, Eugene Belilovsky, Pietro Liò, Aaron C. Courville:
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. CoRR abs/1908.04950 (2019) - [i90]Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron C. Courville:
No Press Diplomacy: Modeling Multi-Agent Gameplay. CoRR abs/1909.02128 (2019) - [i89]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. CoRR abs/1910.06711 (2019) - [i88]Shawn Tan, Guillaume Androz, Ahmad Chamseddine, Pierre Fecteau, Aaron C. Courville, Yoshua Bengio, Joseph Paul Cohen:
Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery. CoRR abs/1910.09570 (2019) - [i87]Yikang Shen, Shawn Tan, Seyedarian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron C. Courville:
Ordered Memory. CoRR abs/1910.13466 (2019) - [i86]Sara Hooker, Aaron C. Courville, Yann N. Dauphin, Andrea Frome:
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning. CoRR abs/1911.05248 (2019) - [i85]Dzmitry Bahdanau, Harm de Vries, Timothy J. O'Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, Aaron C. Courville:
CLOSURE: Assessing Systematic Generalization of CLEVR Models. CoRR abs/1912.05783 (2019) - 2018
- [i84]Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, R. Devon Hjelm, Aaron C. Courville:
MINE: Mutual Information Neural Estimation. CoRR abs/1801.04062 (2018) - [i83]Mohamed Ishmael Belghazi, Sai Rajeswar, Olivier Mastropietro, Negar Rostamzadeh, Jovana Mitrovic, Aaron C. Courville:
Hierarchical Adversarially Learned Inference. CoRR abs/1802.01071 (2018) - [i82]Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville:
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. CoRR abs/1802.10151 (2018) - [i81]Yikang Shen, Shawn Tan, Chin-Wei Huang, Aaron C. Courville:
Generating Contradictory, Neutral, and Entailing Sentences. CoRR abs/1803.02710 (2018) - [i80]Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron C. Courville:
Neural Autoregressive Flows. CoRR abs/1804.00779 (2018) - [i79]Yikang Shen, Zhouhan Lin, Athul Paul Jacob, Alessandro Sordoni, Aaron C. Courville, Yoshua Bengio:
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance. CoRR abs/1806.04168 (2018) - [i78]Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas, Yoshua Bengio:
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. CoRR abs/1806.05236 (2018) - [i77]Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron C. Courville:
Learning Distributed Representations from Reviews for Collaborative Filtering. CoRR abs/1806.06875 (2018) - [i76]Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville:
On the Spectral Bias of Deep Neural Networks. CoRR abs/1806.08734 (2018) - [i75]Florian Strub, Mathieu Seurin, Ethan Perez, Harm de Vries, Jérémie Mary, Philippe Preux, Aaron C. Courville, Olivier Pietquin:
Visual Reasoning with Multi-hop Feature Modulation. CoRR abs/1808.04446 (2018) - [i74]Adrien Ali Taïga, Aaron C. Courville, Marc G. Bellemare:
Approximate Exploration through State Abstraction. CoRR abs/1808.09819 (2018) - [i73]Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron C. Courville:
Improving Explorability in Variational Inference with Annealed Variational Objectives. CoRR abs/1809.01818 (2018) - [i72]Remi Tachet des Combes, Mohammad Pezeshki, Samira Shabanian, Aaron C. Courville, Yoshua Bengio:
On the Learning Dynamics of Deep Neural Networks. CoRR abs/1809.06848 (2018) - [i71]Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron C. Courville:
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. CoRR abs/1810.09536 (2018) - [i70]Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron C. Courville:
Blindfold Baselines for Embodied QA. CoRR abs/1811.05013 (2018) - [i69]Kyle Kastner, João Felipe Santos, Yoshua Bengio, Aaron C. Courville:
Representation Mixing for TTS Synthesis. CoRR abs/1811.07240 (2018) - [i68]Kyle Kastner, Rithesh Kumar, Tim Cooijmans, Aaron C. Courville:
Harmonic Recomposition using Conditional Autoregressive Modeling. CoRR abs/1811.07426 (2018) - [i67]Johanna Hansen, Kyle Kastner, Aaron C. Courville, Gregory Dudek:
Planning in Dynamic Environments with Conditional Autoregressive Models. CoRR abs/1811.10097 (2018) - [i66]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? CoRR abs/1811.12889 (2018) - [i65]Lucas Caccia, Herke van Hoof, Aaron C. Courville, Joelle Pineau:
Deep Generative Modeling of LiDAR Data. CoRR abs/1812.01180 (2018) - 2017
- [i64]Ying Zhang, Mohammad Pezeshki, Philemon Brakel, Saizheng Zhang, César Laurent, Yoshua Bengio, Aaron C. Courville:
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. CoRR abs/1701.02720 (2017) - [i63]Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville:
Calibrating Energy-based Generative Adversarial Networks. CoRR abs/1702.01691 (2017) - [i62]Florian Strub, Harm de Vries, Jérémie Mary, Bilal Piot, Aaron C. Courville, Olivier Pietquin:
End-to-end optimization of goal-driven and visually grounded dialogue systems. CoRR abs/1703.05423 (2017) - [i61]Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, Aaron C. Courville:
Improved Training of Wasserstein GANs. CoRR abs/1704.00028 (2017) - [i60]Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Joseph Pal, Aaron C. Courville:
Adversarial Generation of Natural Language. CoRR abs/1705.10929 (2017) - [i59]Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien:
A Closer Look at Memorization in Deep Networks. CoRR abs/1706.05394 (2017) - [i58]Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C. Courville:
Modulating early visual processing by language. CoRR abs/1707.00683 (2017) - [i57]Ethan Perez, Harm de Vries, Florian Strub, Vincent Dumoulin, Aaron C. Courville:
Learning Visual Reasoning Without Strong Priors. CoRR abs/1707.03017 (2017) - [i56]Yikang Shen, Shawn Tan, Christopher Joseph Pal, Aaron C. Courville:
Self-organized Hierarchical Softmax. CoRR abs/1707.08588 (2017) - [i55]Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron C. Courville:
FiLM: Visual Reasoning with a General Conditioning Layer. CoRR abs/1709.07871 (2017) - [i54]Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron C. Courville:
Learnable Explicit Density for Continuous Latent Space and Variational Inference. CoRR abs/1710.02248 (2017) - [i53]David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron C. Courville:
Bayesian Hypernetworks. CoRR abs/1710.04759 (2017) - [i52]Yikang Shen, Zhouhan Lin, Chin-Wei Huang, Aaron C. Courville:
Neural Language Modeling by Jointly Learning Syntax and Lexicon. CoRR abs/1711.02013 (2017) - [i51]Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron C. Courville:
HoME: a Household Multimodal Environment. CoRR abs/1711.11017 (2017) - [i50]Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio:
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. CoRR abs/1712.04120 (2017) - 2016
- [i49]Alex Lamb, Vincent Dumoulin, Aaron C. Courville:
Discriminative Regularization for Generative Models. CoRR abs/1602.03220 (2016) - [i48]Iulian Vlad Serban, Alberto García-Durán, Çaglar Gülçehre, Sungjin Ahn, Sarath Chandar, Aaron C. Courville, Yoshua Bengio:
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus. CoRR abs/1603.06807 (2016) - [i47]Tim Cooijmans, Nicolas Ballas, César Laurent, Aaron C. Courville:
Recurrent Batch Normalization. CoRR abs/1603.09025 (2016) - [i46]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, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang:
Theano: A Python framework for fast computation of mathematical expressions. CoRR abs/1605.02688 (2016) - [i45]Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher J. Pal, Hugo Larochelle, Aaron C. Courville, Bernt Schiele:
Movie Description. CoRR abs/1605.03705 (2016) - [i44]Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C. Courville, Yoshua Bengio:
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. CoRR abs/1605.06069 (2016) - [i43]Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville:
Adversarially Learned Inference. CoRR abs/1606.00704 (2016) - [i42]Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron C. Courville:
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation. CoRR abs/1606.00776 (2016) - [i41]David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. CoRR abs/1606.01305 (2016) - [i40]Amjad Almahairi, Kyunghyun Cho, Nizar Habash, Aaron C. Courville:
First Result on Arabic Neural Machine Translation. CoRR abs/1606.02680 (2016) - [i39]Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, Yoshua Bengio:
An Actor-Critic Algorithm for Sequence Prediction. CoRR abs/1607.07086 (2016) - [i38]Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio:
Professor Forcing: A New Algorithm for Training Recurrent Networks. CoRR abs/1610.09038 (2016) - [i37]Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taïga, Francesco Visin, David Vázquez, Aaron C. Courville:
PixelVAE: A Latent Variable Model for Natural Images. CoRR abs/1611.05013 (2016) - [i36]Tegan Maharaj, Nicolas Ballas, Aaron C. Courville, Christopher Joseph Pal:
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. CoRR abs/1611.07810 (2016) - [i35]Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron C. Courville:
GuessWhat?! Visual object discovery through multi-modal dialogue. CoRR abs/1611.08481 (2016) - [i34]Iulian Vlad Serban, Alexander G. Ororbia II, Joelle Pineau, Aaron C. Courville:
Multi-modal Variational Encoder-Decoders. CoRR abs/1612.00377 (2016) - [i33]David Vázquez, Jorge Bernal, Francisco Javier Sánchez, Gloria Fernández-Esparrach, Antonio M. López, Adriana Romero, Michal Drozdzal, Aaron C. Courville:
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images. CoRR abs/1612.00799 (2016) - [i32]Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Generalizable Features From Unsupervised Learning. CoRR abs/1612.03809 (2016) - [i31]Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron C. Courville, Yoshua Bengio:
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. CoRR abs/1612.07837 (2016) - 2015
- [i30]Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio:
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. CoRR abs/1502.03044 (2015) - [i29]Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Joseph Pal, Hugo Larochelle, Aaron C. Courville:
Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism. CoRR abs/1502.08029 (2015) - [i28]Atousa Torabi, Christopher J. Pal, Hugo Larochelle, Aaron C. Courville:
Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research. CoRR abs/1503.01070 (2015) - [i27]Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin, Çaglar Gülçehre, Vincent Michalski, Kishore Reddy Konda, Sébastien Jean, Pierre Froumenty, Yann N. Dauphin, Nicolas Boulanger-Lewandowski, Raul Chandias Ferrari, Mehdi Mirza, David Warde-Farley, Aaron C. Courville, Pascal Vincent, Roland Memisevic, Christopher J. Pal, Yoshua Bengio:
EmoNets: Multimodal deep learning approaches for emotion recognition in video. CoRR abs/1503.01800 (2015) - [i26]Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron C. Courville, Yoshua Bengio:
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks. CoRR abs/1505.00393 (2015) - [i25]Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron C. Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle:
Brain Tumor Segmentation with Deep Neural Networks. CoRR abs/1505.03540 (2015) - [i24]Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio:
A Recurrent Latent Variable Model for Sequential Data. CoRR abs/1506.02216 (2015) - [i23]KyungHyun Cho, Aaron C. Courville, Yoshua Bengio:
Describing Multimedia Content using Attention-based Encoder-Decoder Networks. CoRR abs/1507.01053 (2015) - [i22]Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C. Courville, Joelle Pineau:
Hierarchical Neural Network Generative Models for Movie Dialogues. CoRR abs/1507.04808 (2015) - [i21]Marcin Moczulski, Kelvin Xu, Aaron C. Courville, KyungHyun Cho:
A Controller Recognizer Framework: How necessary is recognition for control? CoRR abs/1511.06428 (2015) - [i20]Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron C. Courville, Yoshua Bengio:
Deconstructing the Ladder Network Architecture. CoRR abs/1511.06430 (2015) - [i19]Dzmitry Bahdanau, Dmitriy Serdyuk, Philemon Brakel, Nan Rosemary Ke, Jan Chorowski, Aaron C. Courville, Yoshua Bengio:
Task Loss Estimation for Sequence Prediction. CoRR abs/1511.06456 (2015) - [i18]Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron C. Courville, Yoshua Bengio:
Variance Reduction in SGD by Distributed Importance Sampling. CoRR abs/1511.06481 (2015) - [i17]Francesco Visin, Kyle Kastner, Aaron C. Courville, Yoshua Bengio, Matteo Matteucci, KyungHyun Cho:
ReSeg: A Recurrent Neural Network for Object Segmentation. CoRR abs/1511.07053 (2015) - [i16]Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron C. Courville:
Dynamic Capacity Networks. CoRR abs/1511.07838 (2015) - 2014
- [i15]Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio:
Generative Adversarial Networks. CoRR abs/1406.2661 (2014) - [i14]Guillaume Desjardins, Heng Luo, Aaron C. Courville, Yoshua Bengio:
Deep Tempering. CoRR abs/1410.0123 (2014) - 2013
- [i13]Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, Yoshua Bengio:
Maxout Networks. CoRR abs/1302.4389 (2013) - [i12]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, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio:
Challenges in Representation Learning: A report on three machine learning contests. CoRR abs/1307.0414 (2013) - [i11]Yoshua Bengio, Nicholas Léonard, Aaron C. Courville:
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. CoRR abs/1308.3432 (2013) - [i10]Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
On the Challenges of Physical Implementations of RBMs. CoRR abs/1312.5258 (2013) - 2012
- [i9]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery. CoRR abs/1201.3382 (2012) - [i8]Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio:
On Training Deep Boltzmann Machines. CoRR abs/1203.4416 (2012) - [i7]Yoshua Bengio, Aaron C. Courville, Pascal Vincent:
Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives. CoRR abs/1206.5538 (2012) - [i6]Olivier Delalleau, Aaron C. Courville, Yoshua Bengio:
Efficient EM Training of Gaussian Mixtures with Missing Data. CoRR abs/1209.0521 (2012) - [i5]Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio:
Disentangling Factors of Variation via Generative Entangling. CoRR abs/1210.5474 (2012) - [i4]Heng Luo, Pierre Luc Carrier, Aaron C. Courville, Yoshua Bengio:
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions. CoRR abs/1211.5687 (2012) - [i3]Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
Joint Training of Deep Boltzmann Machines. CoRR abs/1212.2686 (2012) - 2011
- [i2]James Bergstra, Aaron C. Courville, Yoshua Bengio:
The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All). CoRR abs/1109.6638 (2011) - 2010
- [i1]Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio:
Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs. CoRR abs/1012.3476 (2010)
Coauthor Index
[b1] [j16] [j14] [j11] [j10] [j9] [j8] [j7] [j6] [j5] [j3] [c154] [c145] [c143] [c135] [c131] [c128] [c125] [c122] [c121] [c113] [c101] [c96] [c86] [c80] [c76] [c73] [c67] [c63] [c62] [c57] [c51] [c50] [c49] [c48] [c47] [c44] [c40] [c39] [c38] [c36] [c34] [c33] [c30] [c29] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [p1] [i166] [i165] [i162] [i154] [i148] [i144] [i143] [i128] [i127] [i119] [i110] [i107] [i102] [i89] [i88] [i85] [i79] [i78] [i76] [i72] [i69] [i64] [i59] [i50] [i48] [i46] [i44] [i42] [i41] [i39] [i38] [i32] [i31] [i30] [i27] [i26] [i25] [i24] [i23] [i22] [i20] [i19] [i18] [i17] [i15] [i14] [i13] [i12] [i11] [i10] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [i2] [i1]
Kyunghyun Cho
aka: KyungHyun Cho
aka: KyungHyun Cho
Samuel Lavoie-Marchildon
aka: Samuel Lavoie
aka: Samuel Lavoie
Johan S. Obando-Ceron
aka: Johan Samir Obando-Ceron
aka: Johan Samir Obando-Ceron
Sai Rajeswar
aka: Sai Rajeswar Mudumba
aka: Sai Rajeswar Mudumba
Iulian Serban
aka: Iulian Vlad Serban
aka: Iulian Vlad Serban
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