Mahesh Chandra Mukkamala

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Matthias HeinProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Peter OchsUniversity of TübingenVerified email at math.uni-tuebingen.de
Quynh NguyenMax Planck Institute for Mathematics in the SciencesVerified email at mis.mpg.de
Thomas PockProfessor of Computer Science, TU GrazVerified email at icg.tugraz.at
Shoham SabachAssociate ProfessorVerified email at ie.technion.ac.il
Emanuel LaudePhD Student, Technical University of MunichVerified email at in.tum.de
Daniel CremersTechnical University of MunichVerified email at tum.de
Felix WesterkampTU MünchenVerified email at tum.de
Jalal FadiliCNRS-ENSICAEN-Université CaenVerified email at greyc.ensicaen.fr

Mahesh Chandra Mukkamala

Mathematical Optimization Group, University of Tübingen

Verified email at math.uni-tuebingen.de - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds MC Mukkamala, M Hein ICML 2017, 2017	141	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	43	2019
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions Q Nguyen, MC Mukkamala, M Hein ICML 2018, 2018	24	2018
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	14	2020
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms MC Mukkamala, P Ochs NeurIPS 2019, 2019	8	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	2	2019
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms MC Mukkamala, J Fadili, P Ochs arXiv preprint arXiv:2012.13161, 2020		2020

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