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Mahesh Chandra Mukkamala
Mahesh Chandra Mukkamala
Mathematical Consultant | Mathematical Optimization, Machine Learning and Applied Mathematics
Verified email at math.uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
MC Mukkamala, M Hein
ICML 2017, 2017
2412017
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
642019
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Q Nguyen, MC Mukkamala, M Hein
ICML 2018, 2018
352018
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
332020
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
MC Mukkamala, P Ochs
NeurIPS 2019, 2019
172019
Bregman proximal framework for deep linear neural networks
MC Mukkamala, F Westerkamp, E Laude, D Cremers, P Ochs
arXiv preprint arXiv:1910.03638, 2019
62019
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
42022
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
Awesome Open Source Awesome Open Source
MC Mukkamala, P Ochs, BPG CoCaIn
2019
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Articles 1–10