| Variants of RMSProp and Adagrad with Logarithmic Regret Bounds MC Mukkamala, M Hein ICML 2017, 2017 | 400 | 2017 |
| On the loss landscape of a class of deep neural networks with no bad local valleys Q Nguyen, MC Mukkamala, M Hein ICLR 2019, 2019 | 108 | 2019 |
| Convex-concave backtracking for inertial Bregman proximal gradient algorithms in nonconvex optimization MC Mukkamala, P Ochs, T Pock, S Sabach SIAM Journal on Mathematics of Data Science 2 (3), 658-682, 2020 | 71 | 2020 |
| Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions Q Nguyen, MC Mukkamala, M Hein ICML 2018, 2018 | 67 | 2018 |
| Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms MC Mukkamala, P Ochs NeurIPS 2019, 2019 | 37 | 2019 |
| Bregman proximal framework for deep linear neural networks MC Mukkamala, F Westerkamp, E Laude, D Cremers, P Ochs arXiv preprint arXiv:1910.03638, 2019 | 14 | 2019 |
| Global convergence of model function based Bregman proximal minimization algorithms MC Mukkamala, J Fadili, P Ochs Journal of Global Optimization 83 (4), 753-781, 2022 | 13 | 2022 |
| Bregman proximal minimization algorithms, analysis and applications MC Mukkamala Dissertation, Tübingen, Universität Tübingen, 2021, 2021 | 2 | 2021 |
| Bregman Proximal Gradient Algorithms for Deep Matrix Factorization MC Mukkamala, F Westerkamp, Laude, Emanuel, D Cremers, P Ochs Scale Space and Variational Methods in Computer Vision: 8th International …, 0 | 1* | |
Matthias HeinProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
