110 citations · 203 across the 7 of their papers we have counts for
7 papers
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms
Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs
Lipschitz continuity of the gradient mapping of a continuously differentiable function plays a crucial role in designing various optimization algorithms. However, many functions ar…
Bregman Proximal Framework for Deep Linear Neural Networks
Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude +2
A typical assumption for the analysis of first order optimization methods is the Lipschitz continuity of the gradient of the objective function. However, for many practical applica…
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
Mahesh Chandra Mukkamala, Peter Ochs
Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. They usually suffer from the major drawback that the…
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock +1
Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple…
On the loss landscape of a class of deep neural networks with no bad local valleys
Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that…
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
In the recent literature the important role of depth in deep learning has been emphasized. In this paper we argue that sufficient width of a feedforward network is equally importan…
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Mahesh Chandra Mukkamala, Matthias Hein
Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analy…