b'Rene Vidal'

research

∙ 04/11/2022

Structured Graph Variational Autoencoders for Indoor Furniture layout Generation

We present a structured graph variational autoencoder for generating the...

Aditya Chattopadhyay, et al. ∙

research

∙ 03/28/2022

ARCS: Accurate Rotation and Correspondence Search

This paper is about the old Wahba problem in its more general form, whic...

Liangzu Peng, et al. ∙

research

∙ 03/09/2022

Reverse Engineering ℓ_p attacks: A block-sparse optimization approach with recovery guarantees

Deep neural network-based classifiers have been shown to be vulnerable t...

Darshan Thaker, et al. ∙

research

∙ 01/22/2022

Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension

Robust subspace recovery (RSR) is a fundamental problem in robust repres...

Paris V. Giampouras, et al. ∙

research

∙ 01/19/2022

Prospective Learning: Back to the Future

Research on both natural intelligence (NI) and artificial intelligence (...

Joshua T. Vogelstein, et al. ∙

research

∙ 10/08/2021

Learning a Self-Expressive Network for Subspace Clustering

State-of-the-art subspace clustering methods are based on self-expressiv...

Shangzhi Zhang, et al. ∙

research

∙ 10/06/2021

Boosting RANSAC via Dual Principal Component Pursuit

In this paper, we revisit the problem of local optimization in RANSAC. O...

Yunchen Yang, et al. ∙

research

∙ 08/19/2021

Lifelong Computing

Computing systems form the backbone of many aspects of our life, hence t...

Danny Weyns, et al. ∙

research

∙ 05/13/2021

On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks

Neural networks trained via gradient descent with random initialization ...

Hancheng Min, et al. ∙

research

∙ 11/30/2020

Doubly Stochastic Subspace Clustering

Many state-of-the-art subspace clustering methods follow a two-step proc...

Derek Lim, et al. ∙

research

∙ 10/26/2020

A novel variational form of the Schatten-p quasi-norm

The Schatten-p quasi-norm with p∈(0,1) has recently gained considerable ...

Paris Giampouras, et al. ∙

research

∙ 10/08/2020

A Critique of Self-Expressive Deep Subspace Clustering

Subspace clustering is an unsupervised clustering technique designed to ...

Benjamin D. Haeffele, et al. ∙

research

∙ 09/14/2020

A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses

Research in adversarial learning follows a cat and mouse game between at...

Ambar Pal, et al. ∙

research

∙ 06/07/2020

Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces

Finding a small set of representatives from an unlabeled dataset is a co...

Chong You, et al. ∙

research

∙ 05/08/2020

Is an Affine Constraint Needed for Affine Subspace Clustering?

Subspace clustering methods based on expressing each data point as a lin...

Chong You, et al. ∙

research

∙ 04/15/2020

On Dissipative Symplectic Integration with Applications to Gradient-Based Optimization

Continuous-time dynamical systems have proved useful in providing concep...

Guilherme França, et al. ∙

research

∙ 01/20/2020

Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications

The problem of finding the sparsest vector (direction) in a low dimensio...

Qing Qu, et al. ∙

research

∙ 12/30/2019

Basis Pursuit and Orthogonal Matching Pursuit for Subspace-preserving Recovery: Theoretical Analysis

Given an overcomplete dictionary A and a signal b = Ac^* for some sparse...

Daniel P. Robinson, et al. ∙

research

∙ 10/30/2019

On the Regularization Properties of Structured Dropout

Dropout and its extensions (eg. DropBlock and DropConnect) are popular h...

Ambar Pal, et al. ∙

research

∙ 10/12/2019

Generalized Nullspace Property for Structurally Sparse Signals

We propose a new framework for studying the exact recovery of signals wi...

Mustafa D. Kaba, et al. ∙

research

∙ 10/09/2019

The fastest ℓ_1,∞ prox in the west

Proximal operators are of particular interest in optimization problems d...

Benjamín Béjar, et al. ∙

research

∙ 08/02/2019

Gradient Flows and Accelerated Proximal Splitting Methods

Proximal based methods are well-suited to nonsmooth optimization problem...

Guilherme França, et al. ∙

research

∙ 05/17/2019

Neural Message Passing on Hybrid Spatio-Temporal Visual and Symbolic Graphs for Video Understanding

Many problems in video understanding require labeling multiple activitie...

Effrosyni Mavroudi, et al. ∙

research

∙ 03/11/2019

Conformal Symplectic and Relativistic Optimization

Although momentum-based optimization methods have had a remarkable impac...

Guilherme França, et al. ∙

research

∙ 12/24/2018

Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms

Recent methods for learning a linear subspace from data corrupted by out...

Zhihui Zhu, et al. ∙

research

∙ 09/24/2018

Nonconvex Robust Low-rank Matrix Recovery

In this paper we study the problem of recovering a low-rank matrix from ...

Xiao Li, et al. ∙

research

∙ 08/17/2018

On Geometric Analysis of Affine Sparse Subspace Clustering

Sparse subspace clustering (SSC) is a state-of-the-art method for segmen...

Chun-Guang Li, et al. ∙

research

∙ 08/13/2018

Relax, and Accelerate: A Continuous Perspective on ADMM

The acceleration technique first introduced by Nesterov for gradient des...

Guilherme França, et al. ∙

research

∙ 07/19/2018

Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks

We consider the task of estimating the 3D orientation of an object of kn...

Siddharth Mahendran, et al. ∙

research

∙ 07/15/2018

Separable Dictionary Learning with Global Optimality and Applications to Diffusion MRI

Dictionary learning is a popular class of methods for modeling complex d...

Evan Schwab, et al. ∙

research

∙ 06/26/2018

On the Implicit Bias of Dropout

Algorithmic approaches endow deep learning systems with implicit bias th...

Poorya Mianjy, et al. ∙

research

∙ 05/08/2018

A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images

3D pose estimation from a single 2D image is an important and challengin...

Siddharth Mahendran, et al. ∙

research

∙ 03/26/2018

Sparse Recovery over Graph Incidence Matrices: Polynomial Time Guarantees and Location Dependent Performance

Classical results in sparse recovery guarantee the exact reconstruction ...

Mengnan Zhao, et al. ∙

research

∙ 01/29/2018

End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding

Fine-grained action segmentation and recognition is an important yet cha...

Effrosyni Mavroudi, et al. ∙

research

∙ 01/01/2018

Theoretical Analysis of Sparse Subspace Clustering with Missing Entries

Sparse Subspace Clustering (SSC) is a popular unsupervised machine learn...

Manolis C. Tsakiris, et al. ∙

research

∙ 12/13/2017

Mathematics of Deep Learning

Recently there has been a dramatic increase in the performance of recogn...

Rene Vidal, et al. ∙

research

∙ 12/06/2017

Stretching Domain Adaptation: How far is too far?

While deep learning has led to significant advances in visual recognitio...

Yunhan Zhao, et al. ∙

research

∙ 11/20/2017

Joint Object Category and 3D Pose Estimation from 2D Images

2D object detection is the task of finding (i) what objects are present ...

Siddharth Mahendran, et al. ∙

research

∙ 10/13/2017

Dropout as a Low-Rank Regularizer for Matrix Factorization

Regularization for matrix factorization (MF) and approximation problems ...

Jacopo Cavazza, et al. ∙

research

∙ 10/10/2017

An Analysis of Dropout for Matrix Factorization

Dropout is a simple yet effective algorithm for regularizing neural netw...

Jacopo Cavazza, et al. ∙

research

∙ 08/25/2017

Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications

Recently, convex formulations of low-rank matrix factorization problems ...

Benjamin D. Haeffele, et al. ∙

research

∙ 07/21/2017

(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior

Advanced diffusion magnetic resonance imaging (dMRI) techniques, like di...

Evan Schwab, et al. ∙

research

∙ 06/06/2017

Hyperplane Clustering Via Dual Principal Component Pursuit

We extend the theoretical analysis of a recently proposed single subspac...

Manolis C. Tsakiris, et al. ∙

research

∙ 04/12/2017

Provable Self-Representation Based Outlier Detection in a Union of Subspaces

Many computer vision tasks involve processing large amounts of data cont...

Chong You, et al. ∙

research

∙ 03/18/2017

Curriculum Dropout

Dropout is a very effective way of regularizing neural networks. Stochas...

Pietro Morerio, et al. ∙

research

∙ 01/09/2017

Information Pursuit: A Bayesian Framework for Sequential Scene Parsing

Despite enormous progress in object detection and classification, the pr...

Ehsan Jahangiri, et al. ∙

research

∙ 12/18/2016

Joint Spatial-Angular Sparse Coding for dMRI with Separable Dictionaries

Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers...

Evan Schwab, et al. ∙

research

∙ 11/16/2016

Temporal Convolutional Networks for Action Segmentation and Detection

The ability to identify and temporally segment fine-grained human action...

Colin Lea, et al. ∙

research

∙ 10/17/2016

Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework

Subspace clustering refers to the problem of segmenting data drawn from ...

Chun-Guang Li, et al. ∙

research

∙ 08/29/2016

Temporal Convolutional Networks: A Unified Approach to Action Segmentation

The dominant paradigm for video-based action segmentation is composed of...

Colin Lea, et al. ∙

Rene Vidal

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