
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis
We present an efficient stochastic algorithm (RSG+) for canonical correl...
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Simpler Certified Radius Maximization by Propagating Covariances
One strategy for adversarially training a robust model is to maximize it...
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Nyströmformer: A NyströmBased Algorithm for Approximating SelfAttention
Transformers have emerged as a powerful tool for a broad range of natura...
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Flowbased Generative Models for Learning Manifold to Manifold Mappings
Many measurements or observations in computer vision and machine learnin...
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CSURE: Shrinkage Estimator and Prototype Classifier for ComplexValued Deep Learning
The JamesStein (JS) shrinkage estimator is a biased estimator that capt...
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ManifoldNorm: Extending normalizations on Riemannian Manifolds
Many measurements in computer vision and machine learning manifest as no...
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Orthogonal Convolutional Neural Networks
The instability and feature redundancy in CNNs hinders further performan...
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A GMM based algorithm to generate pointcloud and its application to neuroimaging
Recent years have witnessed the emergence of 3D medical imaging techniqu...
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An "augmentationfree" rotation invariant classification scheme on pointcloud and its application to neuroimaging
Recent years have witnessed the emergence and increasing popularity of 3...
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POIRot: A rotation invariant omnidirectional pointnet
Pointcloud is an efficient way to represent 3D world. Analysis of point...
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Surreal: ComplexValued Deep Learning as Principled Transformations on a Rotational Lie Group
Complexvalued deep learning has attracted increasing attention in recen...
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Dilated Convolutional Neural Networks for Sequential Manifoldvalued Data
Efforts are underway to study ways via which the power of deep neural ne...
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Spatial Transformer for 3D Points
Point cloud is an efficient representation of 3D visual data, and enable...
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SurReal: Fréchet Mean and Distance Transform for ComplexValued Deep Learning
We develop a novel deep learning architecture for naturally complexvalu...
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ManifoldNet: A Deep Network Framework for Manifoldvalued Data
Deep neural networks have become the main work horse for many tasks invo...
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A mixture model for aggregation of multiple pretrained weak classifiers
Deep networks have gained immense popularity in Computer Vision and othe...
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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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HCNNs: Convolutional Neural Networks for Riemannian Homogeneous Spaces
Convolutional neural networks are ubiquitous in Machine Learning applica...
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Dictionary Learning and Sparse Coding on Statistical Manifolds
In this paper, we propose a novel information theoretic framework for di...
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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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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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Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Principal Component Analysis (PCA) is a fundamental method for estimatin...
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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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An efficient ExactPGA algorithm for constant curvature manifolds
Manifoldvalued datasets are widely encountered in many computer vision ...
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Rudrasis Chakraborty
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