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Umut Simsekli
Umut Simsekli
INRIA - École Normale Supérieure
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
Generalized sliced wasserstein distances
S Kolouri, K Nadjahi, U Simsekli, R Badeau, G Rohde
Advances in neural information processing systems 32, 2019
1942019
A tail-index analysis of stochastic gradient noise in deep neural networks
U Simsekli, L Sagun, M Gurbuzbalaban
International Conference on Machine Learning, 5827-5837, 2019
1642019
Generalised coupled tensor factorisation
K Yılmaz, A Cemgil, U Simsekli
Advances in neural information processing systems 24, 2011
1262011
Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
A Liutkus, U Simsekli, S Majewski, A Durmus, FR Stöter
International Conference on Machine Learning, 4104-4113, 2019
962019
The heavy-tail phenomenon in SGD
M Gurbuzbalaban, U Simsekli, L Zhu
International Conference on Machine Learning, 3964-3975, 2021
642021
Stochastic Quasi-Newton Langevin Monte Carlo
U Simsekli, R Badeau, AT Cemgil, G Richard
International Conference on Machine Learning, 2016
642016
Learning the morphology of brain signals using alpha-stable convolutional sparse coding
M Jas, T Dupré la Tour, U Simsekli, A Gramfort
Advances in Neural Information Processing Systems 30, 2017
522017
Asymptotic guarantees for learning generative models with the sliced-Wasserstein distance
K Nadjahi, A Durmus, U Simsekli, R Badeau
Advances in Neural Information Processing Systems 32, 2019
512019
Alpha-stable matrix factorization
U Şimşekli, A Liutkus, AT Cemgil
IEEE Signal Processing Letters 22 (12), 2289-2293, 2015
512015
A generative model for non-intrusive load monitoring in commercial buildings
S Henriet, U Şimşekli, B Fuentes, G Richard
Energy and Buildings 177, 268-278, 2018
442018
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo
U Şimşekli
International Conference on Machine Learning (ICML), 2017
442017
Statistical and topological properties of sliced probability divergences
K Nadjahi, A Durmus, L Chizat, S Kolouri, S Shahrampour, U Simsekli
Advances in Neural Information Processing Systems 33, 20802-20812, 2020
432020
On the heavy-tailed theory of stochastic gradient descent for deep neural networks
U Şimşekli, M Gürbüzbalaban, TH Nguyen, G Richard, L Sagun
arXiv preprint arXiv:1912.00018, 2019
42*2019
Speech enhancement with variational autoencoders and alpha-stable distributions
S Leglaive, U Şimşekli, A Liutkus, L Girin, R Horaud
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019
402019
Score guided musical source separation using generalized coupled tensor factorization
U Şimşekli, AT Cemgil
2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO …, 2012
402012
Fractional underdamped langevin dynamics: Retargeting sgd with momentum under heavy-tailed gradient noise
U Simsekli, L Zhu, YW Teh, M Gurbuzbalaban
International conference on machine learning, 8970-8980, 2020
392020
Hausdorff dimension, heavy tails, and generalization in neural networks
U Simsekli, O Sener, G Deligiannidis, MA Erdogdu
Advances in Neural Information Processing Systems 33, 5138-5151, 2020
392020
First exit time analysis of stochastic gradient descent under heavy-tailed gradient noise
TH Nguyen, U Simsekli, M Gurbuzbalaban, G Richard
Advances in neural information processing systems 32, 2019
372019
Explicit regularisation in gaussian noise injections
A Camuto, M Willetts, U Simsekli, SJ Roberts, CC Holmes
Advances in neural information processing systems 33, 16603-16614, 2020
362020
Stochastic gradient richardson-romberg markov chain monte carlo
A Durmus, U Simsekli, E Moulines, R Badeau, G Richard
Advances in Neural Information Processing Systems 29, 2016
352016
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