E-LIB Suche - Ergebnisse für: Mukkamala, Mahesh Chandra

1

Global convergence of model function based Bregman proximal..:

Mukkamala, Mahesh Chandra ; Fadili, Jalal ; Ochs, Peter
Journal of Global Optimization. 83 (2021) 4 - p. 753-781 , 2021

Link: https://doi.org/10.1007/..

2

Bregman Proximal Gradient Algorithms for Deep Matrix Factor..:

, In: Lecture Notes in Computer Science; Scale Space and Variational Methods in Computer Vision,

Mukkamala, Mahesh Chandra ; Westerkamp, Felix ; Laude, Emanuel.. - p. 204-215 , 2021

Link: https://doi.org/10.1007/..

3

Convex-Concave Backtracking for Inertial Bregman Proximal G..:

Mukkamala, Mahesh Chandra ; Ochs, Peter ; Pock, Thomas.
SIAM Journal on Mathematics of Data Science. 2 (2020) 3 - p. 658-682 , 2020

Link: https://doi.org/10.1137/..

4

Global Convergence of Model Function Based Bregman Proximal..:

Mukkamala, Mahesh Chandra ; Fadili, Jalal ; Ochs, Peter
http://arxiv.org/abs/2012.13161. , 2020

Link: http://arxiv.org/abs/201..

5

Beyond Alternating Updates for Matrix Factorization with In..:

Mukkamala, Mahesh Chandra ; Ochs, Peter
http://arxiv.org/abs/1905.09050. , 2019

Link: http://arxiv.org/abs/190..

6

Convex-Concave Backtracking for Inertial Bregman Proximal G..:

Mukkamala, Mahesh Chandra ; Ochs, Peter ; Pock, Thomas.
http://arxiv.org/abs/1904.03537. , 2019

Link: http://arxiv.org/abs/190..

7

Bregman Proximal Framework for Deep Linear Neural Networks:

Mukkamala, Mahesh Chandra ; Westerkamp, Felix ; Laude, Emanuel..
http://arxiv.org/abs/1910.03638. , 2019

Link: http://arxiv.org/abs/191..

8

On the loss landscape of a class of deep neural networks wi..:

Nguyen, Quynh ; Mukkamala, Mahesh Chandra ; Hein, Matthias
http://arxiv.org/abs/1809.10749. , 2018

Link: http://arxiv.org/abs/180..

9

Neural Networks Should Be Wide Enough to Learn Disconnected..:

Nguyen, Quynh ; Mukkamala, Mahesh Chandra ; Hein, Matthias
http://arxiv.org/abs/1803.00094. , 2018

Link: http://arxiv.org/abs/180..

10

Variants of RMSProp and Adagrad with Logarithmic Regret Bou..:

Mukkamala, Mahesh Chandra ; Hein, Matthias
http://arxiv.org/abs/1706.05507. , 2017

Link: http://arxiv.org/abs/170..

11

Global Convergence of Model Function Based Bregman Proximal..:

Chandra Mukkamala, Mahesh ; Fadili, Jalal M ; Ochs, Peter
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10898-021-01114-y. , 2021

Link: https://hal.science/hal-..

12

Global Convergence of Model Function Based Bregman Proximal..:

Chandra Mukkamala, Mahesh ; Fadili, Jalal M ; Ochs, Peter
hal-03452326. , 2021

Link: https://hal.archives-ouv..

13

Bregman proximal minimization algorithms, analysis and appl..:

Mukkamala, Mahesh Chandra , 2021

Link: http://nbn-resolving.de/..

14

Approaches for Efficient Query Optimization Using Semantic ..:

, In: Innovations in Computer Science and Engineering; Lecture Notes in Networks and Systems,

Mukkamala, Rambabu ; Purna Chandra Rao, V. - p. 423-436 , 2020

Link: https://doi.org/10.1007/..

15

Enhanced two photon absorption cross section and optical no..:

Namboodiri, C.K.R. ; Bongu, S.R. ; Bisht, P.B....
Journal of Photochemistry and Photobiology A: Chemistry. 314 (2016) - p. 60-65 , 2016

Link: https://doi.org/10.1016/..