| Improved Training of Wasserstein GANs I Gulrajani, F Ahmed, M Arjovsky, V Dumoulin, A Courville Neural Information Processing Systems, 2017 | 7269 | 2017 |
| A guide to convolution arithmetic for deep learning V Dumoulin, F Visin arXiv preprint arXiv:1603.07285, 2016 | 1646 | 2016 |
| Adversarially Learned Inference V Dumoulin, I Belghazi, B Poole, A Lamb, M Arjovsky, O Mastropietro, ... International Conference on Learning Representations (ICLR), 2017 | 1339 | 2017 |
| Generative adversarial networks: An overview A Creswell, T White, V Dumoulin, K Arulkumaran, B Sengupta, ... IEEE signal processing magazine 35 (1), 53-65, 2018 | 1298 | 2018 |
| Deconvolution and checkerboard artifacts A Odena, V Dumoulin, C Olah Distill 1 (10), e3, 2016 | 1277 | 2016 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 992* | 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 | 883 | 2017 |
| FiLM: Visual Reasoning with a General Conditioning Layer E Perez, F Strub, H de Vries, V Dumoulin, A Courville AAAI 2018, 2017 | 845 | 2017 |
| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... International Conference on Learning Representations (ICLR), 2019 | 367 | 2019 |
| Pylearn2: a machine learning research library IJ Goodfellow, D Warde-Farley, P Lamblin, V Dumoulin, M Mirza, ... arXiv preprint arXiv:1308.4214, 2013 | 360 | 2013 |
| 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 | 218 | 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 | 198 | 2020 |
| Blocks and fuel: Frameworks for deep learning B Van Merriënboer, D Bahdanau, V Dumoulin, D Serdyuk, ... arXiv preprint arXiv:1506.00619, 2015 | 190 | 2015 |
| Discriminative regularization for generative models A Lamb, V Dumoulin, A Courville arXiv preprint arXiv:1602.03220, 2016 | 79 | 2016 |
| A guide to convolution arithmetic for deep learning. arXiv 2016 V Dumoulin, F Visin arXiv preprint arXiv:1603.07285, 0 | 66 | |
| Feature-wise transformations V Dumoulin, E Perez, N Schucher, F Strub, H Vries, A Courville, Y Bengio Distill 3 (7), e11, 2018 | 65* | 2018 |
| On the challenges of physical implementations of RBMs V Dumoulin, IJ Goodfellow, A Courville, Y Bengio AAAI 2014, 2014 | 64 | 2014 |
| 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 | 60 | 2017 |
| A guide to convolution arithmetic for deep learning (2016) V Dumoulin, F Visin arXiv preprint arXiv:1603.07285, 2016 | 59 | 2016 |
| Harm de Vries, Aaron Courville, and Yoshua Bengio. Feature-wise transformations. Distill, 2018 V Dumoulin, E Perez, N Schucher, F Strub | 52 | |
Aaron CourvilleAssociate Professor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified email at umontreal.ca
