| Learning Representations and Generative Models for 3D Point Clouds P Achlioptas, O Diamanti, I Mitliagkas, L Guibas International Conference on Machine Learning, 2018 | 1713 | 2018 |
| Manifold mixup: Better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, I Mitliagkas, A Courville, ... arXiv preprint arXiv:1806.05236, 2018 | 1570 | 2018 |
| Manifold mixup: Better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, I Mitliagkas, A Courville, ... arXiv preprint arXiv:1806.05236, 2018 | 1570 | 2018 |
| Invariance principle meets information bottleneck for out-of-distribution generalization K Ahuja, E Caballero, D Zhang, JC Gagnon-Audet, Y Bengio, I Mitliagkas, ... Advances in Neural Information Processing Systems 34, 3438-3450, 2021 | 299 | 2021 |
| Gotta go fast when generating data with score-based models A Jolicoeur-Martineau, K Li, R Piché-Taillefer, T Kachman, I Mitliagkas arXiv preprint arXiv:2105.14080, 2021 | 245 | 2021 |
| A modern take on the bias-variance tradeoff in neural networks B Neal, S Mittal, A Baratin, V Tantia, M Scicluna, S Lacoste-Julien, ... arXiv preprint arXiv:1810.08591, 2018 | 229 | 2018 |
| Negative momentum for improved game dynamics G Gidel, RA Hemmat, M Pezeshki, G Huang, R Lepriol, S Lacoste-Julien, ... Artificial Intelligence and Statistics, 2019 | 216 | 2019 |
| Generalizing to unseen domains via distribution matching I Albuquerque, J Monteiro, M Darvishi, TH Falk, I Mitliagkas arXiv preprint arXiv:1911.00804, 2019 | 214* | 2019 |
| Memory limited, streaming PCA I Mitliagkas, C Caramanis, P Jain Advances in neural information processing systems 26, 2013 | 201 | 2013 |
| Asynchrony begets momentum, with an application to deep learning I Mitliagkas, C Zhang, S Hadjis, C Ré 2016 54th Annual Allerton Conference on Communication, Control, and …, 2016 | 169 | 2016 |
| Adversarial score matching and improved sampling for image generation A Jolicoeur-Martineau, R Piché-Taillefer, RT Combes, I Mitliagkas arXiv preprint arXiv:2009.05475, 2020 | 140 | 2020 |
| Parallel SGD: When does averaging help? J Zhang, C De Sa, I Mitliagkas, C Ré arXiv preprint arXiv:1606.07365, 2016 | 128 | 2016 |
| Yellowfin and the art of momentum tuning J Zhang, I Mitliagkas SysML, 2019 | 121 | 2019 |
| In search of robust measures of generalization GK Dziugaite, A Drouin, B Neal, N Rajkumar, E Caballero, L Wang, ... Advances in Neural Information Processing Systems 33, 11723-11733, 2020 | 118 | 2020 |
| A tight and unified analysis of gradient-based methods for a whole spectrum of differentiable games W Azizian, I Mitliagkas, S Lacoste-Julien, G Gidel International conference on artificial intelligence and statistics, 2863-2873, 2020 | 117 | 2020 |
| Deep learning at 15pf: supervised and semi-supervised classification for scientific data T Kurth, J Zhang, N Satish, E Racah, I Mitliagkas, MMA Patwary, T Malas, ... Proceedings of the International Conference for High Performance Computing …, 2017 | 103 | 2017 |
| Accelerated stochastic power iteration P Xu, B He, C De Sa, I Mitliagkas, C Re International Conference on Artificial Intelligence and Statistics, 58-67, 2018 | 101 | 2018 |
| Representation learning and adversarial generation of 3D point clouds P Achlioptas, O Diamanti, I Mitliagkas, L Guibas arXiv preprint arXiv:1707.02392, 2017 | 96 | 2017 |
| Joint power and admission control for ad-hoc and cognitive underlay networks: Convex approximation and distributed implementation I Mitliagkas, ND Sidiropoulos, A Swami IEEE Transactions on Wireless Communications 10 (12), 4110-4121, 2011 | 91 | 2011 |
| Multi-objective training of Generative Adversarial Networks with multiple discriminators I Albuquerque, J Monteiro, T Doan, B Considine, T Falk, I Mitliagkas International Conference on Machine Learning, 2019 | 87 | 2019 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARumontreal.ca의 이메일 확인됨
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / Milairo.umontreal.ca의 이메일 확인됨
Alex LambMicrosoft Research (NYC), Université de Montréal, Google Brain, Amazon, Twitch PhD Fellowmicrosoft.com의 이메일 확인됨
Gauthier GidelAssistant professor at Mila, University of Montréal (DIRO)umontreal.ca의 이메일 확인됨
Alexia Jolicoeur-MartineauSamsung - SAIT AI Lab Montrealmail.mcgill.ca의 이메일 확인됨
Leonidas GuibasProfessor of Computer Science, Stanford Universitycs.stanford.edu의 이메일 확인됨
Panos AchlioptasOumi.aistanford.edu의 이메일 확인됨
Christopher BeckhamInstitut Québécois d'Intelligence Artificiellepolymtl.ca의 이메일 확인됨
Vikas VermaAalto University/ MILA/ Google Braingoogle.com의 이메일 확인됨
Joao MonteiroResearch Scientist, Autodeskautodesk.com의 이메일 확인됨
Christopher RéComputer Science, Stanford Universitycs.stanford.edu의 이메일 확인됨
Brady NealPhD Student, Mila - Quebec Artificial Intelligence Institutemila.quebec의 이메일 확인됨
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT Austinutexas.edu의 이메일 확인됨
Amir NajafiAssistant Professor, Computer Engineering Dept. at Sharif University of Technologysharif.edu의 이메일 확인됨
Nicolas LoizouJohns Hopkins Universityjhu.edu의 이메일 확인됨
Kartik AhujaFAIR, Metameta.com의 이메일 확인됨
Divyat MahajanPh.D. Student, Milamila.quebec의 이메일 확인됨
Ethan CaballeroMila (Montreal Institute for Learning Algorithms)mila.quebec의 이메일 확인됨
David Lopez-PazFAIR at Metafb.com의 이메일 확인됨
Rémi Piché-TailleferMilaumontreal.ca의 이메일 확인됨