Ioannis (Yannis) Mitliagkas
Ioannis (Yannis) Mitliagkas
Assistant Professor at Mila, University of Montréal
Adresse e-mail validée de iro.umontreal.ca - Page d'accueil
Titre
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Année
Learning Representations and Generative Models for 3D Point Clouds
P Achlioptas, O Diamanti, I Mitliagkas, L Guibas
International Conference on Machine Learning, 2018
3522018
Memory limited, streaming PCA
I Mitliagkas, C Caramanis, P Jain
Advances in neural information processing systems 26, 2886-2894, 2013
1562013
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
1482018
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
1482018
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
972016
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
792011
Negative momentum for improved game dynamics
G Gidel, RA Hemmat, M Pezeshki, G Huang, R Lepriol, S Lacoste-Julien, ...
Artificial Intelligence and Statistics, 2019
752019
Yellowfin and the art of momentum tuning
J Zhang, I Mitliagkas
SysML, 2019
732019
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
672017
Representation learning and adversarial generation of 3D point clouds
P Achlioptas, O Diamanti, I Mitliagkas, L Guibas
arXiv preprint arXiv:1707.02392, 2017
602017
Parallel SGD: When does averaging help?
J Zhang, C De Sa, I Mitliagkas, C Ré
arXiv preprint arXiv:1606.07365, 2016
552016
Manifold mixup: Learning better representations by interpolating hidden states
V Verma, A Lamb, C Beckham, A Najafi, A Courville, I Mitliagkas, ...
54*2018
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
512018
Omnivore: An optimizer for multi-device deep learning on cpus and gpus
S Hadjis, C Zhang, I Mitliagkas, D Iter, C Ré
arXiv preprint arXiv:1606.04487, 2016
442016
Accelerated stochastic power iteration
C De Sa, B He, I Mitliagkas, C Ré, P Xu
Proceedings of machine learning research 84, 58, 2018
402018
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
312020
Convex approximation-based joint power and admission control for cognitive underlay networks
I Mitliagkas, ND Sidiropoulos, A Swami
2008 International Wireless Communications and Mobile Computing Conference …, 2008
312008
FrogWild!--fast PageRank approximations on graph engines
I Mitliagkas, M Borokhovich, AG Dimakis, C Caramanis
arXiv preprint arXiv:1502.04281, 2015
302015
Fortified networks: Improving the robustness of deep networks by modeling the manifold of hidden representations
A Lamb, J Binas, A Goyal, D Serdyuk, S Subramanian, I Mitliagkas, ...
arXiv preprint arXiv:1804.02485, 2018
292018
Streaming PCA with many missing entries
I Mitliagkas, C Caramanis, P Jain
Preprint, 2014
282014
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