Mahesh Chandra Mukkamala

According to our database, Mahesh Chandra Mukkamala authored at least 9 papers between 2017 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Global convergence of model function based Bregman proximal minimization algorithms.

Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs

J. Glob. Optim., 2022

2021

Bregman Proximal Gradient Algorithms for Deep Matrix Factorization.

Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021

2020

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization.

Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach

SIAM J. Math. Data Sci., 2020

2019

Bregman Proximal Framework for Deep Linear Neural Networks.

Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs

CoRR, 2019

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization.

Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach

CoRR, 2019

Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms.

Mahesh Chandra Mukkamala, Peter Ochs

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the loss landscape of a class of deep neural networks with no bad local valleys.

Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions.

Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds.

Mahesh Chandra Mukkamala, Matthias Hein

Proceedings of the 34th International Conference on Machine Learning, 2017