| Improved Training of Wasserstein GANs I Gulrajani, F Ahmed, M Arjovsky, V Dumoulin, A Courville Neural Information Processing Systems, 2017 | 2303 | 2017 |
| Adversarially Learned Inference V Dumoulin, I Belghazi, B Poole, A Lamb, M Arjovsky, O Mastropietro, ... International Conference on Learning Representations (ICLR), 2017 | 650 | 2017 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688, 2016 | 596 | 2016 |
| A guide to convolution arithmetic for deep learning V Dumoulin, F Visin arXiv preprint arXiv:1603.07285, 2016 | 579 | 2016 |
| Deconvolution and checkerboard artifacts A Odena, V Dumoulin, C Olah Distill 1 (10), e3, 2016 | 487 | 2016 |
| A Learned Representation for Artistic Style V Dumoulin, J Shlens, M Kudlur, A Behboodi, F Lemic, A Wolisz, ... International Conference on Learning Representations (ICLR), 2017 | 332 | 2017 |
| Pylearn2: a machine learning research library IJ Goodfellow, D Warde-Farley, P Lamblin, V Dumoulin, M Mirza, ... arXiv preprint arXiv:1308.4214, 2013 | 292 | 2013 |
| Generative adversarial networks: An overview A Creswell, T White, V Dumoulin, K Arulkumaran, B Sengupta, ... IEEE Signal Processing Magazine 35 (1), 53-65, 2018 | 223 | 2018 |
| FiLM: Visual Reasoning with a General Conditioning Layer E Perez, F Strub, H de Vries, V Dumoulin, A Courville AAAI 2018, 2017 | 186 | 2017 |
| Blocks and fuel: Frameworks for deep learning B Van Merriënboer, D Bahdanau, V Dumoulin, D Serdyuk, ... arXiv preprint arXiv:1506.00619, 2015 | 155 | 2015 |
| Exploring the structure of a real-time, arbitrary neural artistic stylization network G Ghiasi, H Lee, M Kudlur, V Dumoulin, J Shlens British Machine Vision Conference (BMVC), 2017 | 84 | 2017 |
| Discriminative regularization for generative models A Lamb, V Dumoulin, A Courville arXiv preprint arXiv:1602.03220, 2016 | 56 | 2016 |
| On the challenges of physical implementations of RBMs V Dumoulin, IJ Goodfellow, A Courville, Y Bengio AAAI 2014, 2014 | 35 | 2014 |
| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, K Xu, R Goroshin, ... arXiv preprint arXiv:1903.03096, 2019 | 29 | 2019 |
| Learning visual reasoning without strong priors E Perez, H de Vries, F Strub, V Dumoulin, A Courville ICML Workshop on Machine Learning in Speech and Language Processing, 2017 | 29 | 2017 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 23 | 2020 |
| A fast model for the reconstruction of spectral solar irradiance in the near-and mid-ultraviolet C Bolduc, P Charbonneau, V Dumoulin, MS Bourqui, AD Crouch Solar Physics 279 (2), 383-409, 2012 | 18 | 2012 |
| Feature-wise transformations V Dumoulin, E Perez, N Schucher, F Strub, H Vries, A Courville, Y Bengio Distill 3 (7), e11, 2018 | 16* | 2018 |
| A Ballistic Monte Carlo Approximation of {\pi} V Dumoulin, F Thouin arXiv preprint arXiv:1404.1499, 2014 | 2 | 2014 |
| Cross-Modulation Networks for Few-Shot Learning H Prol, V Dumoulin, L Herranz arXiv preprint arXiv:1812.00273, 2018 | 1 | 2018 |
Aaron CourvilleUniversité de MontréalVerified email at umontreal.ca
