| Generalized sliced wasserstein distances S Kolouri, K Nadjahi, U Simsekli, R Badeau, G Rohde Advances in neural information processing systems 32, 2019 | 359 | 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 | 279 | 2019 |
| 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 | 152 | 2019 |
| The heavy-tail phenomenon in SGD M Gurbuzbalaban, U Simsekli, L Zhu International Conference on Machine Learning, 3964-3975, 2021 | 151 | 2021 |
| Generalised coupled tensor factorisation K Yılmaz, A Cemgil, U Simsekli Advances in neural information processing systems 24, 2011 | 128 | 2011 |
| 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 | 99 | 2020 |
| 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 | 80 | 2019 |
| Stochastic Quasi-Newton Langevin Monte Carlo U Simsekli, R Badeau, AT Cemgil, G Richard International Conference on Machine Learning, 2016 | 78 | 2016 |
| 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 | 72 | 2020 |
| 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 | 70* | 2020 |
| Intrinsic dimension, persistent homology and generalization in neural networks T Birdal, A Lou, LJ Guibas, U Simsekli Advances in neural information processing systems 34, 6776-6789, 2021 | 69 | 2021 |
| 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 | 66 | 2018 |
| 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 | 63 | 2017 |
| Groove2groove: One-shot music style transfer with supervision from synthetic data O Cífka, U Şimşekli, G Richard IEEE/ACM Transactions on Audio, Speech, and Language Processing 28, 2638-2650, 2020 | 62 | 2020 |
| 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 | 61 | 2019 |
| 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 | 59 | 2020 |
| Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo U Şimşekli International Conference on Machine Learning (ICML), 2017 | 58 | 2017 |
| Relative positional encoding for transformers with linear complexity A Liutkus, O Cıfka, SL Wu, U Simsekli, YH Yang, G Richard International Conference on Machine Learning, 7067-7079, 2021 | 55 | 2021 |
| 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 | 52* | 2019 |
| Alpha-stable matrix factorization U Şimşekli, A Liutkus, AT Cemgil IEEE Signal Processing Letters 22 (12), 2289-2293, 2015 | 51 | 2015 |
Ali Taylan CemgilResearch Scientist, DeepMind UKgoogle.com의 이메일 확인됨
