| Generalized sliced wasserstein distances S Kolouri, K Nadjahi, U Simsekli, R Badeau, G Rohde Advances in neural information processing systems 32, 2019 | 155 | 2019 |
| 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 | 138 | 2019 |
| Generalised coupled tensor factorisation K Yılmaz, A Cemgil, U Simsekli Advances in neural information processing systems 24, 2011 | 124 | 2011 |
| 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 | 73 | 2019 |
| Stochastic Quasi-Newton Langevin Monte Carlo U Simsekli, R Badeau, AT Cemgil, G Richard International Conference on Machine Learning, 2016 | 62 | 2016 |
| The heavy-tail phenomenon in SGD M Gurbuzbalaban, U Simsekli, L Zhu International Conference on Machine Learning, 3964-3975, 2021 | 56 | 2021 |
| Alpha-stable matrix factorization U Şimşekli, A Liutkus, AT Cemgil IEEE Signal Processing Letters 22 (12), 2289-2293, 2015 | 49 | 2015 |
| 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 | 47 | 2017 |
| 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 | 42 | 2019 |
| 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 | 40 | 2012 |
| Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo U Şimşekli International Conference on Machine Learning (ICML), 2017 | 39 | 2017 |
| 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 | 36 | 2019 |
| 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 | 35* | 2019 |
| 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 | 35 | 2016 |
| 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 | 34 | 2020 |
| 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 | 34 | 2018 |
| 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 | 32 | 2020 |
| Bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc T Birdal, U Simsekli, MO Eken, S Ilic Advances in Neural Information Processing Systems 31, 2018 | 32 | 2018 |
| Alpha-stable multichannel audio source separation S Leglaive, U Şimşekli, A Liutkus, R Badeau, G Richard 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 32 | 2017 |
| 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 | 30 | 2019 |
Ali Taylan CemgilResearch Scientist, DeepMind UKVerified email at google.com
