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2020 – today
- 2024
[j3]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. Trans. Mach. Learn. Res. 2024 (2024)- [i31]Ben Williams, Bart van Merriënboer, Vincent Dumoulin, Jenny Hamer, Eleni Triantafillou, Abram B. Fleishman, Matthew McKown, Jill E. Munger, Aaron N. Rice, Ashlee Lillis, Clemency E. White, Catherine A. D. Hobbs, Tries B. Razak, Kate E. Jones, Tom Denton:
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics. CoRR abs/2404.16436 (2024) - [i30]Nazanin Mohammadi Sepahvand, Vincent Dumoulin, Eleni Triantafillou, Gintare Karolina Dziugaite:
Data Selection for Transfer Unlearning. CoRR abs/2405.10425 (2024) - [i29]Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa, Jamie Hayes, Meghdad Kurmanji, Kairan Zhao, Vincent Dumoulin, Júlio C. S. Jacques Júnior, Ioannis Mitliagkas, Jun Wan, Lisheng Sun-Hosoya, Sergio Escalera, Gintare Karolina Dziugaite, Peter Triantafillou, Isabelle Guyon:
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR abs/2406.09073 (2024) - [i28]Vincent Dumoulin, Wenjing Rao, Natasha Devroye:
On the Response Entropy of APUFs. CoRR abs/2406.19975 (2024) - 2023
- [c17]Vincent Dumoulin, Wenjing Rao, Natasha Devroye:
Active learning for fast and slow modeling attacks on Arbiter PUFs. DSD 2023: 260-268 - [c16]Malik Boudiaf, Tom Denton, Bart van Merrienboer, Vincent Dumoulin, Eleni Triantafillou:
In Search for a Generalizable Method for Source Free Domain Adaptation. ICML 2023: 2914-2931 - [i27]Malik Boudiaf, Tom Denton, Bart van Merriënboer, Vincent Dumoulin, Eleni Triantafillou:
In Search for a Generalizable Method for Source Free Domain Adaptation. CoRR abs/2302.06658 (2023) - [i26]Vincent Dumoulin, Wenjing Rao, Natasha Devroye:
Active learning for fast and slow modeling attacks on Arbiter PUFs. CoRR abs/2308.13645 (2023) - [i25]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. CoRR abs/2311.14115 (2023) - [i24]Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer, Stefan Kahl, Holger Klinck, Tom Denton, Vincent Dumoulin:
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics. CoRR abs/2312.07439 (2023) - 2022
- [c15]Cristina Nader Vasconcelos, Vighnesh Birodkar, Vincent Dumoulin:
Proper Reuse of Image Classification Features Improves Object Detection. CVPR 2022: 13618-13627 - [c14]Yeqi Wei, Tim Fox, Vincent Dumoulin, Wenjing Rao
, Natasha Devroye:
APUF Faults: Impact, Testing, and Diagnosis. DATE 2022: 442-447 - [c13]Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer:
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. ICML 2022: 6009-6033 - [c12]Halley Young, Vincent Dumoulin, Pablo S. Castro, Jesse H. Engel, Cheng-Zhi Anna Huang:
Compositional Steering of Music Transformers 66-80. IUI Workshops 2022: 66-80 - [i23]Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer:
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. CoRR abs/2201.03529 (2022) - [i22]Cristina Nader Vasconcelos, Vighnesh Birodkar, Vincent Dumoulin:
Proper Reuse of Image Classification Features Improves Object Detection. CoRR abs/2204.00484 (2022) - 2021
- [c11]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin:
Impact of Aliasing on Generalization in Deep Convolutional Networks. ICCV 2021: 10509-10518 - [c10]Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin:
Learning a Universal Template for Few-shot Dataset Generalization. ICML 2021: 10424-10433 - [c9]Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle:
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. NeurIPS Datasets and Benchmarks 2021 - [i21]Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle:
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. CoRR abs/2104.02638 (2021) - [i20]Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin:
Learning a Universal Template for Few-shot Dataset Generalization. CoRR abs/2105.07029 (2021) - [i19]João Monteiro, Xavier Gibert Serra, Jianqiao Feng, Vincent Dumoulin, Dar-Shyang Lee:
Domain Conditional Predictors for Domain Adaptation. CoRR abs/2106.13899 (2021) - [i18]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin:
Impact of Aliasing on Generalization in Deep Convolutional Networks. CoRR abs/2108.03489 (2021) - 2020
- [j2]Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling:
The Hanabi challenge: A new frontier for AI research. Artif. Intell. 280: 103216 (2020) - [c8]Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle:
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. ICLR 2020 - [c7]João Monteiro, Xavier Gibert Serra, Jianqiao Feng, Vincent Dumoulin, Dar-Shyang Lee:
Domain Conditional Predictors for Domain Adaptation. Preregister@NeurIPS 2020: 193-220 - [i17]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux, Ross Goroshin:
An Effective Anti-Aliasing Approach for Residual Networks. CoRR abs/2011.10675 (2020)
2010 – 2019
- 2019
- [i16]Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling:
The Hanabi Challenge: A New Frontier for AI Research. CoRR abs/1902.00506 (2019) - [i15]Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle:
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. CoRR abs/1903.03096 (2019) - 2018
- [j1]Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath:
Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 35(1): 53-65 (2018) - [c6]Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron C. Courville:
FiLM: Visual Reasoning with a General Conditioning Layer. AAAI 2018: 3942-3951 - [i14]Hugo Prol, Vincent Dumoulin, Luis Herranz:
Cross-Modulation Networks for Few-Shot Learning. CoRR abs/1812.00273 (2018) - 2017
- [c5]Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens:
Exploring the structure of a real-time, arbitrary neural artistic stylization network. BMVC 2017 - [c4]Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville:
Adversarially Learned Inference. ICLR (Poster) 2017 - [c3]Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur:
A Learned Representation For Artistic Style. ICLR (Poster) 2017 - [c2]Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, Aaron C. Courville:
Improved Training of Wasserstein GANs. NIPS 2017: 5767-5777 - [i13]Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, Aaron C. Courville:
Improved Training of Wasserstein GANs. CoRR abs/1704.00028 (2017) - [i12]Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens:
Exploring the structure of a real-time, arbitrary neural artistic stylization network. CoRR abs/1705.06830 (2017) - [i11]Ethan Perez, Harm de Vries, Florian Strub, Vincent Dumoulin, Aaron C. Courville:
Learning Visual Reasoning Without Strong Priors. CoRR abs/1707.03017 (2017) - [i10]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) - [i9]Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath:
Generative Adversarial Networks: An Overview. CoRR abs/1710.07035 (2017) - 2016
- [i8]Alex Lamb, Vincent Dumoulin, Aaron C. Courville:
Discriminative Regularization for Generative Models. CoRR abs/1602.03220 (2016) - [i7]Vincent Dumoulin, Francesco Visin:
A guide to convolution arithmetic for deep learning. CoRR abs/1603.07285 (2016) - [i6]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) - [i5]Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville:
Adversarially Learned Inference. CoRR abs/1606.00704 (2016) - [i4]Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur:
A Learned Representation For Artistic Style. CoRR abs/1610.07629 (2016) - 2015
- [i3]Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio:
Blocks and Fuel: Frameworks for deep learning. CoRR abs/1506.00619 (2015) - 2014
- [c1]Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
On the Challenges of Physical Implementations of RBMs. AAAI 2014: 1199-1205 - 2013
- [i2]Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, Yoshua Bengio:
Pylearn2: a machine learning research library. CoRR abs/1308.4214 (2013) - [i1]Vincent Dumoulin, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio:
On the Challenges of Physical Implementations of RBMs. CoRR abs/1312.5258 (2013)
Coauthor Index
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