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05/26/2023
Higher Order Gauge Equivariant CNNs on Riemannian Manifolds and Applications
With the advent of group equivariant convolutions in deep networks liter...
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05/21/2023
Kernel Stein Discrepancy on Lie Groups: Theory and Applications
Distributional approximation is a fundamental problem in machine learnin...
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02/14/2023
Horocycle Decision Boundaries for Large Margin Classification in Hyperbolic Space
Hyperbolic spaces have been quite popular in the recent past for represe...
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09/17/2022
A Framework for Improving the Characterization Scope of Stein's Method on Riemannian Manifolds
Stein's method has been widely used to achieve distributional approximat...
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12/03/2021
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design
Hyperbolic neural networks have been popular in the recent past due to t...
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10/27/2020
Nested Grassmanns for Dimensionality Reduction
Grassmann manifolds have been widely used to represent the geometry of f...
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09/28/2020
Shrinkage Estimation of the Frechet Mean in Lie groups
Data in non-Euclidean spaces are commonly encountered in many fields of ...
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07/04/2020
An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices
The James-Stein estimator is an estimator of the multivariate normal mea...
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03/02/2020
MVC-Net: A Convolutional Neural Network Architecture for Manifold-Valued Images With Applications
Geometric deep learning has attracted significant attention in recent ye...
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09/11/2018
ManifoldNet: A Deep Network Framework for Manifold-valued Data
Deep neural networks have become the main work horse for many tasks invo...
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05/31/2018
A mixture model for aggregation of multiple pre-trained weak classifiers
Deep networks have gained immense popularity in Computer Vision and othe...
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05/29/2018
Statistical Recurrent Models on Manifold valued Data
In a number of disciplines, the data (e.g., graphs, manifolds) to be ana...
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05/14/2018
H-CNNs: Convolutional Neural Networks for Riemannian Homogeneous Spaces
Convolutional neural networks are ubiquitous in Machine Learning applica...
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05/03/2018
Dictionary Learning and Sparse Coding on Statistical Manifolds
In this paper, we propose a novel information theoretic framework for di...
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02/03/2017
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Principal Component Analysis (PCA) is a fundamental method for estimatin...
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04/23/2016
An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds
In this work, we propose a novel information theoretic framework for dic...
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03/13/2016