Almost sure convergence of dropout algorithms for neural networks A Senen-Cerda, J Sanders arXiv preprint arXiv:2002.02247, 2020 | 13 | 2020 |
Universal approximation in dropout neural networks OA Manita, MA Peletier, JW Portegies, J Sanders, A Senen-Cerda Journal of Machine Learning Research 23 (19), 1-46, 2022 | 11 | 2022 |
Spectral norm bounds for block Markov chain random matrices J Sanders, A Senen–Cerda Stochastic Processes and their Applications 158, 134-169, 2023 | 10 | 2023 |
Asymptotic convergence rate of Dropout on shallow linear neural networks A Senen-Cerda, J Sanders Proceedings of the ACM on Measurement and Analysis of Computing Systems 6 (2 …, 2022 | 7 | 2022 |
Detection and Evaluation of Clusters within Sequential Data A Van Werde, A Senen-Cerda, G Kosmella, J Sanders arXiv preprint arXiv:2210.01679, 2022 | 5 | 2022 |
Universal approximation in dropout neural networks OA Manita, MA Peletier, JW Portegies, J Sanders, A Senen-Cerda arXiv preprint arXiv:2012.10351, 2020 | 2 | 2020 |
Asymptotic convergence rate of dropout on shallow linear neural networks A Senen-Cerda, J Sanders arXiv preprint arXiv:2012.01978, 2020 | 1 | 2020 |
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions C Comte, M Jonckheere, J Sanders, A Senen-Cerda | | 2023 |
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems C Comte, M Jonckheere, J Sanders, A Senen-Cerda arXiv preprint arXiv:2312.02804, 2023 | | 2023 |