
Doubly Stochastic Subspace Clustering
Many stateoftheart subspace clustering methods follow a twostep proc...
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A novel variational form of the Schattenp quasinorm
The Schattenp quasinorm with p∈(0,1) has recently gained considerable ...
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A Critique of SelfExpressive Deep Subspace Clustering
Subspace clustering is an unsupervised clustering technique designed to ...
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A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Research in adversarial learning follows a cat and mouse game between at...
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SelfRepresentation Based Unsupervised Exemplar Selection in a Union of Subspaces
Finding a small set of representatives from an unlabeled dataset is a co...
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Is an Affine Constraint Needed for Affine Subspace Clustering?
Subspace clustering methods based on expressing each data point as a lin...
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On Dissipative Symplectic Integration with Applications to GradientBased Optimization
Continuoustime dynamical systems have proved useful in providing concep...
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Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications
The problem of finding the sparsest vector (direction) in a low dimensio...
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Basis Pursuit and Orthogonal Matching Pursuit for Subspacepreserving Recovery: Theoretical Analysis
Given an overcomplete dictionary A and a signal b = Ac^* for some sparse...
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On the Regularization Properties of Structured Dropout
Dropout and its extensions (eg. DropBlock and DropConnect) are popular h...
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Generalized Nullspace Property for Structurally Sparse Signals
We propose a new framework for studying the exact recovery of signals wi...
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The fastest ℓ_1,∞ prox in the west
Proximal operators are of particular interest in optimization problems d...
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Gradient Flows and Accelerated Proximal Splitting Methods
Proximal based methods are wellsuited to nonsmooth optimization problem...
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Neural Message Passing on Hybrid SpatioTemporal Visual and Symbolic Graphs for Video Understanding
Many problems in video understanding require labeling multiple activitie...
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Conformal Symplectic and Relativistic Optimization
Although momentumbased optimization methods have had a remarkable impac...
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Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms
Recent methods for learning a linear subspace from data corrupted by out...
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Nonconvex Robust Lowrank Matrix Recovery
In this paper we study the problem of recovering a lowrank matrix from ...
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On Geometric Analysis of Affine Sparse Subspace Clustering
Sparse subspace clustering (SSC) is a stateoftheart method for segmen...
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Relax, and Accelerate: A Continuous Perspective on ADMM
The acceleration technique first introduced by Nesterov for gradient des...
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Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks
We consider the task of estimating the 3D orientation of an object of kn...
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Separable Dictionary Learning with Global Optimality and Applications to Diffusion MRI
Dictionary learning is a popular class of methods for modeling complex d...
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On the Implicit Bias of Dropout
Algorithmic approaches endow deep learning systems with implicit bias th...
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A Mixed ClassificationRegression Framework for 3D Pose Estimation from 2D Images
3D pose estimation from a single 2D image is an important and challengin...
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Sparse Recovery over Graph Incidence Matrices: Polynomial Time Guarantees and Location Dependent Performance
Classical results in sparse recovery guarantee the exact reconstruction ...
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EndtoEnd FineGrained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding
Finegrained action segmentation and recognition is an important yet cha...
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Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Sparse Subspace Clustering (SSC) is a popular unsupervised machine learn...
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Mathematics of Deep Learning
Recently there has been a dramatic increase in the performance of recogn...
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Stretching Domain Adaptation: How far is too far?
While deep learning has led to significant advances in visual recognitio...
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Joint Object Category and 3D Pose Estimation from 2D Images
2D object detection is the task of finding (i) what objects are present ...
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Dropout as a LowRank Regularizer for Matrix Factorization
Regularization for matrix factorization (MF) and approximation problems ...
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An Analysis of Dropout for Matrix Factorization
Dropout is a simple yet effective algorithm for regularizing neural netw...
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Structured LowRank Matrix Factorization: Global Optimality, Algorithms, and Applications
Recently, convex formulations of lowrank matrix factorization problems ...
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(k,q)Compressed Sensing for dMRI with Joint SpatialAngular Sparsity Prior
Advanced diffusion magnetic resonance imaging (dMRI) techniques, like di...
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Hyperplane Clustering Via Dual Principal Component Pursuit
We extend the theoretical analysis of a recently proposed single subspac...
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Provable SelfRepresentation Based Outlier Detection in a Union of Subspaces
Many computer vision tasks involve processing large amounts of data cont...
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Curriculum Dropout
Dropout is a very effective way of regularizing neural networks. Stochas...
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Information Pursuit: A Bayesian Framework for Sequential Scene Parsing
Despite enormous progress in object detection and classification, the pr...
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Joint SpatialAngular Sparse Coding for dMRI with Separable Dictionaries
Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers...
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Temporal Convolutional Networks for Action Segmentation and Detection
The ability to identify and temporally segment finegrained human action...
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Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework
Subspace clustering refers to the problem of segmenting data drawn from ...
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Temporal Convolutional Networks: A Unified Approach to Action Segmentation
The dominant paradigm for videobased action segmentation is composed of...
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Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
Stateoftheart subspace clustering methods are based on expressing eac...
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Segmental Spatiotemporal CNNs for Finegrained Action Segmentation
Joint segmentation and classification of finegrained actions is importa...
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Car Segmentation and Pose Estimation using 3D Object Models
Image segmentation and 3D pose estimation are two key cogs in any algori...
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Filtrated Spectral Algebraic Subspace Clustering
Algebraic Subspace Clustering (ASC) is a simple and elegant method based...
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Dual Principal Component Pursuit
We consider the problem of outlier rejection in single subspace learning...
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Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
Subspace clustering methods based on ℓ_1, ℓ_2 or nuclear norm regulariza...
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Global Optimality in Tensor Factorization, Deep Learning, and Beyond
Techniques involving factorization are found in a wide range of applicat...
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Filtrated Algebraic Subspace Clustering
Subspace clustering is the problem of clustering data that lie close to ...
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Dynamic Template Tracking and Recognition
In this paper we address the problem of tracking nonrigid objects whose...
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Rene Vidal
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Director of The Johns Hopkins Mathematical Institute for Data Science at The Johns Hopkins University