b'John Wright'

research

∙ 07/23/2022

Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks

Gravitational wave astronomy is a vibrant field that leverages both clas...

7 Jingkai Yan, et al. ∙

research

∙ 03/09/2022

Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization

Achieving invariance to nuisance transformations is a fundamental challe...

5 Sam Buchanan, et al. ∙

research

∙ 01/05/2022

Testing matrix product states

Devising schemes for testing the amount of entanglement in quantum syste...

0 Mehdi Soleimanifar, et al. ∙

research

∙ 11/15/2021

Quantum soundness of testing tensor codes

A locally testable code is an error-correcting code that admits very eff...

0 Zhengfeng Ji, et al. ∙

research

∙ 11/01/2021

Unique Games hardness of Quantum Max-Cut, and a vector-valued Borell's inequality

The Gaussian noise stability of a function f:ℝ^n →{-1, 1} is the expecte...

0 Yeongwoo Hwang, et al. ∙

research

∙ 10/29/2021

Principal Component Pursuit for Pattern Identification in Environmental Mixtures

Environmental health researchers often aim to identify sources/behaviors...

0 Elizabeth A. Gibson, et al. ∙

research

∙ 07/29/2021

Deep Networks Provably Classify Data on Curves

Data with low-dimensional nonlinear structure are ubiquitous in engineer...

6 Tingran Wang, et al. ∙

research

∙ 06/17/2021

Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery

We propose a new framework – Square Root Principal Component Pursuit – f...

3 Junhui Zhang, et al. ∙

research

∙ 05/21/2021

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

This work attempts to provide a plausible theoretical framework that aim...

16 Kwan Ho Ryan Chan, et al. ∙

research

∙ 04/08/2021

Generalized Approach to Matched Filtering using Neural Networks

Gravitational wave science is a pioneering field with rapidly evolving d...

6 Jingkai Yan, et al. ∙

research

∙ 10/27/2020

Deep Networks from the Principle of Rate Reduction

This work attempts to interpret modern deep (convolutional) networks fro...

16 Kwan Ho Ryan Chan, et al. ∙

research

∙ 09/27/2020

Quantum soundness of the classical low individual degree test

Low degree tests play an important role in classical complexity theory, ...

0 Zhengfeng Ji, et al. ∙

research

∙ 08/25/2020

Deep Networks and the Multiple Manifold Problem

We study the multiple manifold problem, a binary classification task mod...

0 Sam Buchanan, et al. ∙

research

∙ 07/14/2020

From Symmetry to Geometry: Tractable Nonconvex Problems

As science and engineering have become increasingly data-driven, the rol...

12 Yuqian Zhang, 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

∙ 01/13/2020

MIP*=RE

We show that the class MIP* of languages that can be decided by a classi...

0 Zhengfeng Ji, et al. ∙

research

∙ 11/22/2019

Gemmini: An Agile Systolic Array Generator Enabling Systematic Evaluations of Deep-Learning Architectures

Advances in deep learning and neural networks have resulted in the rapid...

0 Hasan Genc, et al. ∙

research

∙ 08/28/2019

Short-and-Sparse Deconvolution -- A Geometric Approach

Short-and-sparse deconvolution (SaSD) is the problem of extracting local...

7 Yenson Lau, et al. ∙

research

∙ 06/06/2019

Complete Dictionary Learning via ℓ^4-Norm Maximization over the Orthogonal Group

This paper considers the fundamental problem of learning a complete (ort...

0 Yuexiang Zhai, et al. ∙

research

∙ 04/11/2019

NEEXP in MIP*

We study multiprover interactive proof systems. The power of classical m...

0 Anand Natarajan, et al. ∙

research

∙ 01/07/2019

On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution

Blind deconvolution is the problem of recovering a convolutional kernel ...

0 Yuqian Zhang, et al. ∙

research

∙ 01/02/2019

Geometry and Symmetry in Short-and-Sparse Deconvolution

We study the Short-and-Sparse (SaS) deconvolution problem of recovering ...

0 Han-wen Kuo, et al. ∙

research

∙ 06/01/2018

Structured Local Optima in Sparse Blind Deconvolution

Blind deconvolution is a ubiquitous problem of recovering two unknown si...

0 Yuqian Zhang, et al. ∙

research

∙ 12/03/2017

Convolutional Phase Retrieval via Gradient Descent

We study the convolutional phase retrieval problem, which considers reco...

0 Qing Qu, et al. ∙

research

∙ 02/22/2016

A Geometric Analysis of Phase Retrieval

Can we recover a complex signal from its Fourier magnitudes? More genera...

0 Ju Sun, et al. ∙

research

∙ 11/15/2015

Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method

We consider the problem of recovering a complete (i.e., square and inver...

0 Ju Sun, et al. ∙

research

∙ 11/11/2015

Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture

We consider the problem of recovering a complete (i.e., square and inver...

0 Ju Sun, et al. ∙

research

∙ 10/21/2015

When Are Nonconvex Problems Not Scary?

In this note, we focus on smooth nonconvex optimization problems that ob...

0 Ju Sun, et al. ∙

research

∙ 04/26/2015

Complete Dictionary Recovery over the Sphere

We consider the problem of recovering a complete (i.e., square and inver...

0 Ju Sun, et al. ∙

research

∙ 12/15/2014

Finding a sparse vector in a subspace: Linear sparsity using alternating directions

Is it possible to find the sparsest vector (direction) in a generic subs...

0 Qing Qu, et al. ∙

research

∙ 03/29/2014

Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods

Recovering matrices from compressive and grossly corrupted observations ...

0 Cun Mu, et al. ∙

research

∙ 07/22/2013

Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Recovering a low-rank tensor from incomplete information is a recurring ...

0 Cun Mu, et al. ∙

research

∙ 07/04/2013

Toward Guaranteed Illumination Models for Non-Convex Objects

Illumination variation remains a central challenge in object detection a...

0 Yuqian Zhang, et al. ∙

research

∙ 11/05/2012

Efficient Point-to-Subspace Query in ℓ^1: Theory and Applications in Computer Vision

Motivated by vision tasks such as robust face and object recognition, we...

0 Ju Sun, et al. ∙

research

∙ 08/02/2012

Efficient Point-to-Subspace Query in ℓ^1 with Application to Robust Object Instance Recognition

Motivated by vision tasks such as robust face and object recognition, we...

0 Ju Sun, et al. ∙

research

∙ 11/03/2011

Sparsity and Robustness in Face Recognition

This report concerns the use of techniques for sparse signal representat...

0 John Wright, et al. ∙

John Wright

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