9 citations · 19 across the 5 of their papers we have counts for
5 papers
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds
Monami Banerjee, Rudrasis Chakraborty, Jose Bouza +1
Convolutional neural networks have been highly successful in image-based learning tasks due to their translation equivariance property. Recent work has generalized the traditional…
Dictionary Learning and Sparse Coding on Statistical Manifolds
Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
In this paper, we propose a novel information theoretic framework for dictionary learning (DL) and sparse coding (SC) on a statistical manifold (the manifold of probability distrib…
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices
Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen +5
In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are non-Euclidean in nature. Geometric deep learning corresponds to techniques that generalize deep ne…
A CNN for homogneous Riemannian manifolds with applications to Neuroimaging
Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
Convolutional neural networks are ubiquitous in Machine Learning applications for solving a variety of problems. They however can not be used in their native form when the domain o…
An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds
Rudrasis Chakraborty, Monami Banerjee, Victoria Crawford +1
In this work, we propose a novel information theoretic framework for dictionary learning (DL) and sparse coding (SC) on a statistical manifold (the manifold of probability distribu…