Rudrasis Chakraborty | DeepAI

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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...

0 Xingjian Zhen, et al. ∙

∙ 03/29/2022

Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets

Pooling multiple neuroimaging datasets across institutions often enables...

0 Vishnu Suresh Lokhande, et al. ∙

∙ 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...

0 Jurijs Nazarovs, et al. ∙

∙ 12/01/2021

Forward Operator Estimation in Generative Models with Kernel Transfer Operators

Generative models which use explicit density modeling (e.g., variational...

4 Zhichun Huang, et al. ∙

∙ 06/08/2021

An Online Riemannian PCA for Stochastic Canonical Correlation Analysis

We present an efficient stochastic algorithm (RSG+) for canonical correl...

0 Zihang Meng, et al. ∙

∙ 04/13/2021

Simpler Certified Radius Maximization by Propagating Covariances

One strategy for adversarially training a robust model is to maximize it...

0 Xingjian Zhen, et al. ∙

∙ 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...

0 Yunyang Xiong, et al. ∙

∙ 12/18/2020

Flow-based Generative Models for Learning Manifold to Manifold Mappings

Many measurements or observations in computer vision and machine learnin...

17 Xingjian Zhen, et al. ∙

∙ 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...

0 Yifei Xing, et al. ∙

∙ 03/30/2020

ManifoldNorm: Extending normalizations on Riemannian Manifolds

Many measurements in computer vision and machine learning manifest as no...

0 Rudrasis Chakraborty, et al. ∙

∙ 11/27/2019

Orthogonal Convolutional Neural Networks

The instability and feature redundancy in CNNs hinders further performan...

0 Jiayun Wang, et al. ∙

∙ 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...

0 Liu Yang, et al. ∙

∙ 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...

0 Liu Yang, et al. ∙

∙ 10/29/2019

POIRot: A rotation invariant omni-directional pointnet

Point-cloud is an efficient way to represent 3D world. Analysis of point...

12 Liu Yang, et al. ∙

∙ 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...

0 Rudrasis Chakraborty, et al. ∙

∙ 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...

0 Xingjian Zhen, et al. ∙

∙ 06/26/2019

Spatial Transformer for 3D Points

Point cloud is an efficient representation of 3D visual data, and enable...

0 Jiayun Wang, et al. ∙

∙ 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...

2 Rudrasis Chakraborty, et al. ∙

∙ 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...

0 Rudrasis Chakraborty, et al. ∙

∙ 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...

0 Rudrasis Chakraborty, et al. ∙

∙ 05/29/2018

Statistical Recurrent Models on Manifold valued Data

In a number of disciplines, the data (e.g., graphs, manifolds) to be ana...

0 Rudrasis Chakraborty, et al. ∙

∙ 05/14/2018

H-CNNs: Convolutional Neural Networks for Riemannian Homogeneous Spaces

Convolutional neural networks are ubiquitous in Machine Learning applica...

0 Rudrasis Chakraborty, et al. ∙

∙ 05/03/2018

Dictionary Learning and Sparse Coding on Statistical Manifolds

In this paper, we propose a novel information theoretic framework for di...

0 Rudrasis Chakraborty, et al. ∙

∙ 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 ...

0 Indrasis Chakraborty, et al. ∙

∙ 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...

0 Rudrasis Chakraborty, et al. ∙

∙ 02/03/2017

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

Principal Component Analysis (PCA) is a fundamental method for estimatin...

0 Rudrasis Chakraborty, et al. ∙

∙ 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...

0 Rudrasis Chakraborty, et al. ∙

∙ 03/13/2016

An efficient Exact-PGA algorithm for constant curvature manifolds

Manifold-valued datasets are widely encountered in many computer vision ...

0 Rudrasis Chakraborty, et al. ∙

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