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07/20/2022
On the Versatile Uses of Partial Distance Correlation in Deep Learning
Comparing the functional behavior of neural network models, whether it i...
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03/29/2022
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets
Pooling multiple neuroimaging datasets across institutions often enables...
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02/18/2022
Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data
Panel data involving longitudinal measurements of the same set of partic...
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12/01/2021
Forward Operator Estimation in Generative Models with Kernel Transfer Operators
Generative models which use explicit density modeling (e.g., variational...
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06/08/2021
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis
We present an efficient stochastic algorithm (RSG+) for canonical correl...
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04/13/2021
Simpler Certified Radius Maximization by Propagating Covariances
One strategy for adversarially training a robust model is to maximize it...
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02/07/2021
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
Transformers have emerged as a powerful tool for a broad range of natura...
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12/18/2020
Flow-based Generative Models for Learning Manifold to Manifold Mappings
Many measurements or observations in computer vision and machine learnin...
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06/22/2020
C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning
The James-Stein (JS) shrinkage estimator is a biased estimator that capt...
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03/30/2020
ManifoldNorm: Extending normalizations on Riemannian Manifolds
Many measurements in computer vision and machine learning manifest as no...
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11/27/2019
Orthogonal Convolutional Neural Networks
The instability and feature redundancy in CNNs hinders further performan...
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11/05/2019
A GMM based algorithm to generate point-cloud and its application to neuroimaging
Recent years have witnessed the emergence of 3D medical imaging techniqu...
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11/05/2019
An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging
Recent years have witnessed the emergence and increasing popularity of 3...
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10/29/2019
POIRot: A rotation invariant omni-directional pointnet
Point-cloud is an efficient way to represent 3D world. Analysis of point...
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10/18/2019
Surreal: Complex-Valued Deep Learning as Principled Transformations on a Rotational Lie Group
Complex-valued deep learning has attracted increasing attention in recen...
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10/05/2019
Dilated Convolutional Neural Networks for Sequential Manifold-valued Data
Efforts are underway to study ways via which the power of deep neural ne...
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06/26/2019
Spatial Transformer for 3D Points
Point cloud is an efficient representation of 3D visual data, and enable...
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06/24/2019
SurReal: Fréchet Mean and Distance Transform for Complex-Valued Deep Learning
We develop a novel deep learning architecture for naturally complex-valu...
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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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04/15/2018
Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems
Fault detection problem for closed loop uncertain dynamical systems, is ...
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07/31/2017
Statistics on the (compact) Stiefel manifold: Theory and Applications
A Stiefel manifold of the compact type is often encountered in many fiel...
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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
