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An Effective Anti-Aliasing Approach for Residual Networks

Nov 20, 2020
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux, Ross Goroshin


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Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Mar 07, 2019
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle


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The Hanabi Challenge: A New Frontier for AI Research

Feb 01, 2019
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

* 37 pages, 5 figures, submitted to Artificial Intelligence 

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Cross-Modulation Networks for Few-Shot Learning

Dec 01, 2018
Hugo Prol, Vincent Dumoulin, Luis Herranz

* Accepted at NIPS 2018 Workshop on Meta-Learning. Source code available at https://github.com/hprop/cross-modulation-nets 

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A guide to convolution arithmetic for deep learning

Jan 11, 2018
Vincent Dumoulin, Francesco Visin


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Improved Training of Wasserstein GANs

Dec 25, 2017
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville

* NIPS camera-ready 

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Learning Visual Reasoning Without Strong Priors

Dec 18, 2017
Ethan Perez, Harm de Vries, Florian Strub, Vincent Dumoulin, Aaron Courville

* Full AAAI 2018 paper is at arXiv:1709.07871. Presented at ICML 2017's Machine Learning in Speech and Language Processing Workshop. Code is at http://github.com/ethanjperez/film 

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FiLM: Visual Reasoning with a General Conditioning Layer

Dec 18, 2017
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron Courville

* AAAI 2018. Code available at http://github.com/ethanjperez/film . Extends arXiv:1707.03017 

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Generative Adversarial Networks: An Overview

Oct 19, 2017
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A Bharath

* Accepted in the IEEE Signal Processing Magazine Special Issue on Deep Learning for Visual Understanding 

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Exploring the structure of a real-time, arbitrary neural artistic stylization network

Aug 24, 2017
Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens

* Accepted as an oral presentation at British Machine Vision Conference (BMVC) 2017 

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Adversarially Learned Inference

Feb 21, 2017
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville


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A Learned Representation For Artistic Style

Feb 09, 2017
Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur

* 9 pages. 15 pages of Appendix, International Conference on Learning Representations (ICLR) 2017 

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Theano: A Python framework for fast computation of mathematical expressions

May 09, 2016
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, 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 Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, 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 Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher 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 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

* 19 pages, 5 figures 

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Discriminative Regularization for Generative Models

Feb 15, 2016
Alex Lamb, Vincent Dumoulin, Aaron Courville


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Blocks and Fuel: Frameworks for deep learning

Jun 01, 2015
Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio


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On the Challenges of Physical Implementations of RBMs

Oct 24, 2014
Vincent Dumoulin, Ian J. Goodfellow, Aaron Courville, Yoshua Bengio

* Proc. AAAI 2014, pp. 1199-1205 

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Pylearn2: a machine learning research library

Aug 20, 2013
Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, Yoshua Bengio

* 9 pages 

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