b'Rene Vidal'

research

∙ 08/24/2023

Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions

Variational Information Pursuit (V-IP) is a framework for making interpr...

0 Kwan Ho Ryan Chan, et al. ∙

research

∙ 07/24/2023

Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization

This paper studies the problem of training a two-layer ReLU network for ...

0 Hancheng Min, et al. ∙

research

∙ 06/08/2023

Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models

The advent of large pre-trained models has brought about a paradigm shif...

0 Tianzhe Chu, et al. ∙

research

∙ 06/07/2023

A Linearly Convergent GAN Inversion-based Algorithm for Reverse Engineering of Deceptions

An important aspect of developing reliable deep learning systems is devi...

0 Darshan Thaker, et al. ∙

research

∙ 06/07/2023

Efficient Vision Transformer for Human Pose Estimation via Patch Selection

While Convolutional Neural Networks (CNNs) have been widely successful i...

0 Kaleab Alemayehu Kinfu, et al. ∙

research

∙ 04/29/2023

The Ideal Continual Learner: An Agent That Never Forgets

The goal of continual learning is to find a model that solves multiple l...

0 Liangzu Peng, et al. ∙

research

∙ 04/11/2023

Necessary and Sufficient Conditions for Simultaneous State and Input Recovery of Linear Systems with Sparse Inputs by ℓ_1-Minimization

The study of theoretical conditions for recovering sparse signals from c...

0 Kyle Poe, et al. ∙

research

∙ 02/06/2023

Variational Information Pursuit for Interpretable Predictions

There is a growing interest in the machine learning community in develop...

0 Aditya Chattopadhyay, et al. ∙

research

∙ 12/01/2022

On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection

State-of-the-art object detectors are fast and accurate, but they requir...

0 Ambar Pal, et al. ∙

research

∙ 11/07/2022

Facial Tic Detection in Untrimmed Videos of Tourette Syndrome Patients

Tourette Syndrome (TS) is a behavior disorder that onsets in childhood a...

0 Yutao Tang, et al. ∙

research

∙ 10/01/2022

Learning Globally Smooth Functions on Manifolds

Smoothness and low dimensional structures play central roles in improvin...

10 Juan Cervino, et al. ∙

research

∙ 07/18/2022

Towards Understanding The Semidefinite Relaxations of Truncated Least-Squares in Robust Rotation Search

The rotation search problem aims to find a 3D rotation that best aligns ...

0 Liangzu Peng, et al. ∙

research

∙ 07/03/2022

Interpretable by Design: Learning Predictors by Composing Interpretable Queries

There is a growing concern about typically opaque decision-making with h...

7 Aditya Chattopadhyay, et al. ∙

research

∙ 06/16/2022

Analysis and Extensions of Adversarial Training for Video Classification

Adversarial training (AT) is a simple yet effective defense against adve...

10 Kaleab Alemayehu Kinfu, et al. ∙

research

∙ 04/11/2022

Structured Graph Variational Autoencoders for Indoor Furniture layout Generation

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

0 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...

0 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...

5 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...

0 Paris V. Giampouras, et al. ∙

research

∙ 01/19/2022

Prospective Learning: Back to the Future

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

8 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...

0 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...

9 Yunchen Yang, et al. ∙

research

∙ 08/19/2021

Lifelong Computing

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

0 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 ...

0 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...

0 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 ...

0 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 ...

0 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...

0 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...

0 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...

0 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...

18 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...

46 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...

0 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...

0 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...

0 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...

17 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...

3 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...

0 Effrosyni Mavroudi, et al. ∙

research

∙ 03/11/2019

Conformal Symplectic and Relativistic Optimization

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

0 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...

7 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 ...

0 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...

0 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...

0 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...

0 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...

0 Evan Schwab, et al. ∙

research

∙ 06/26/2018

On the Implicit Bias of Dropout

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

0 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...

0 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 ...

0 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...

0 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...

0 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...

0 Rene Vidal, et al. ∙

Rene Vidal

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