14 citations · 58 across the 16 of their papers we have counts for
5 papers · 1 filter
ManifoldNet: A Deep Network Framework for Manifold-valued Data
Rudrasis Chakraborty, Jose Bouza, Jonathan Manton +1
Deep neural networks have become the main work horse for many tasks involving learning from data in a variety of applications in Science and Engineering. Traditionally, the input t…
A mixture model for aggregation of multiple pre-trained weak classifiers
Rudrasis Chakraborty, Chun-Hao Yang, Baba C. Vemuri
Deep networks have gained immense popularity in Computer Vision and other fields in the past few years due to their remarkable performance on recognition/classification tasks surpa…
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…