Variants of RMSProp and Adagrad with Logarithmic Regret Bounds MC Mukkamala, M Hein ICML 2017, 2017 | 358 | 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 | 93 | 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 | 60 | 2020 |
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions Q Nguyen, MC Mukkamala, M Hein ICML 2018, 2018 | 60 | 2018 |
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms MC Mukkamala, P Ochs NeurIPS 2019, 2019 | 30 | 2019 |
Global convergence of model function based Bregman proximal minimization algorithms MC Mukkamala, J Fadili, P Ochs Journal of Global Optimization, 1-29, 2022 | 9 | 2022 |
Bregman proximal framework for deep linear neural networks MC Mukkamala, F Westerkamp, E Laude, D Cremers, P Ochs arXiv preprint arXiv:1910.03638, 2019 | 9 | 2019 |
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* | |
Bregman proximal minimization algorithms, analysis and applications MC Mukkamala Universität Tübingen, 2022 | | 2022 |
Cocain Bpg Matrix Factorization MC Mukkamala, P Ochs | | 2019 |