1 code implementation • 5 Jun 2021 • Monami Banerjee, Rudrasis Chakraborty, Jose Bouza, Baba C. Vemuri
In this paper, we present a novel higher order Volterra convolutional neural network (VolterraNet) for data defined as samples of functions on Riemannian homogeneous spaces.
1 code implementation • NeurIPS 2018 • Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David Vaillancourt, Vikas Singh, Baba C. Vemuri
We show how recurrent statistical recurrent network models can be defined in such spaces.
no code implementations • 14 May 2018 • Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
(ii) As a corrolary, we prove the equivariance of the correlation operation to group actions admitted by the input domains which are Riemannian homogeneous manifolds.
no code implementations • 3 May 2018 • 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 distributions).
no code implementations • ICCV 2017 • Monami Banerjee, Rudrasis Chakraborty, Baba C. Vemuri
In this paper, we present a novel generalization of SPCA, called sparse exact PGA (SEPGA) that can cope with manifold-valued input data and respect the intrinsic geometry of the underlying manifold.
no code implementations • CVPR 2016 • Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, Michael S. Okun, David E. Viallancourt, Baba C. Vemuri
With the exception of a few, most existing methods of regression for manifold valued data are limited to geodesic regression which is a generalization of the linear regression in vector-spaces.
no code implementations • 23 Apr 2016 • Rudrasis Chakraborty, Monami Banerjee, Victoria Crawford, Baba C. Vemuri
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 distributions).
no code implementations • ICCV 2015 • Hyunwoo J. Kim, Nagesh Adluru, Monami Banerjee, Baba C. Vemuri, Vikas Singh
Probability density functions (PDFs) are fundamental "objects" in mathematics with numerous applications in computer vision, machine learning and medical imaging.