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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...
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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 ...
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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...
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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...
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06/07/2023
Efficient Vision Transformer for Human Pose Estimation via Patch Selection
While Convolutional Neural Networks (CNNs) have been widely successful i...
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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...
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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...
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02/06/2023
Variational Information Pursuit for Interpretable Predictions
There is a growing interest in the machine learning community in develop...
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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...
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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...
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10/01/2022
Learning Globally Smooth Functions on Manifolds
Smoothness and low dimensional structures play central roles in improvin...
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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 ...
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07/03/2022
Interpretable by Design: Learning Predictors by Composing Interpretable Queries
There is a growing concern about typically opaque decision-making with h...
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06/16/2022
Analysis and Extensions of Adversarial Training for Video Classification
Adversarial training (AT) is a simple yet effective defense against adve...
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04/11/2022
Structured Graph Variational Autoencoders for Indoor Furniture layout Generation
We present a structured graph variational autoencoder for generating the...
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03/28/2022
ARCS: Accurate Rotation and Correspondence Search
This paper is about the old Wahba problem in its more general form, whic...
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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...
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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...
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01/19/2022
Prospective Learning: Back to the Future
Research on both natural intelligence (NI) and artificial intelligence (...
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10/08/2021
Learning a Self-Expressive Network for Subspace Clustering
State-of-the-art subspace clustering methods are based on self-expressiv...
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10/06/2021
Boosting RANSAC via Dual Principal Component Pursuit
In this paper, we revisit the problem of local optimization in RANSAC. O...
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08/19/2021
Lifelong Computing
Computing systems form the backbone of many aspects of our life, hence t...
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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 ...
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11/30/2020
Doubly Stochastic Subspace Clustering
Many state-of-the-art subspace clustering methods follow a two-step proc...
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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 ...
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10/08/2020
A Critique of Self-Expressive Deep Subspace Clustering
Subspace clustering is an unsupervised clustering technique designed to ...
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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...
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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...
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05/08/2020
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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04/15/2020
On Dissipative Symplectic Integration with Applications to Gradient-Based Optimization
Continuous-time dynamical systems have proved useful in providing concep...
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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...
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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...
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10/30/2019
On the Regularization Properties of Structured Dropout
Dropout and its extensions (eg. DropBlock and DropConnect) are popular h...
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10/12/2019
Generalized Nullspace Property for Structurally Sparse Signals
We propose a new framework for studying the exact recovery of signals wi...
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10/09/2019
The fastest ℓ_1,∞ prox in the west
Proximal operators are of particular interest in optimization problems d...
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08/02/2019
Gradient Flows and Accelerated Proximal Splitting Methods
Proximal based methods are well-suited to nonsmooth optimization problem...
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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...
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03/11/2019
Conformal Symplectic and Relativistic Optimization
Although momentum-based optimization methods have had a remarkable impac...
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12/24/2018
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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09/24/2018
Nonconvex Robust Low-rank Matrix Recovery
In this paper we study the problem of recovering a low-rank matrix from ...
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08/17/2018
On Geometric Analysis of Affine Sparse Subspace Clustering
Sparse subspace clustering (SSC) is a state-of-the-art method for segmen...
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08/13/2018
Relax, and Accelerate: A Continuous Perspective on ADMM
The acceleration technique first introduced by Nesterov for gradient des...
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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...
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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...
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06/26/2018
On the Implicit Bias of Dropout
Algorithmic approaches endow deep learning systems with implicit bias th...
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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...
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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 ...
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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...
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01/01/2018
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Sparse Subspace Clustering (SSC) is a popular unsupervised machine learn...
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12/13/2017
