
-
Generalized Approach to Matched Filtering using Neural Networks
Gravitational wave science is a pioneering field with rapidly evolving d...
read it
-
Deep Networks from the Principle of Rate Reduction
This work attempts to interpret modern deep (convolutional) networks fro...
read it
-
Quantum soundness of the classical low individual degree test
Low degree tests play an important role in classical complexity theory, ...
read it
-
Deep Networks and the Multiple Manifold Problem
We study the multiple manifold problem, a binary classification task mod...
read it
-
From Symmetry to Geometry: Tractable Nonconvex Problems
As science and engineering have become increasingly data-driven, the rol...
read it
-
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications
The problem of finding the sparsest vector (direction) in a low dimensio...
read it
-
MIP*=RE
We show that the class MIP* of languages that can be decided by a classi...
read it
-
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...
read it
-
Short-and-Sparse Deconvolution -- A Geometric Approach
Short-and-sparse deconvolution (SaSD) is the problem of extracting local...
read it
-
Complete Dictionary Learning via ℓ^4-Norm Maximization over the Orthogonal Group
This paper considers the fundamental problem of learning a complete (ort...
read it
-
NEEXP in MIP*
We study multiprover interactive proof systems. The power of classical m...
read it
-
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution
Blind deconvolution is the problem of recovering a convolutional kernel ...
read it
-
Geometry and Symmetry in Short-and-Sparse Deconvolution
We study the Short-and-Sparse (SaS) deconvolution problem of recovering ...
read it
-
Structured Local Optima in Sparse Blind Deconvolution
Blind deconvolution is a ubiquitous problem of recovering two unknown si...
read it
-
Convolutional Phase Retrieval via Gradient Descent
We study the convolutional phase retrieval problem, which considers reco...
read it
-
A Geometric Analysis of Phase Retrieval
Can we recover a complex signal from its Fourier magnitudes? More genera...
read it
-
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...
read it
-
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...
read it
-
When Are Nonconvex Problems Not Scary?
In this note, we focus on smooth nonconvex optimization problems that ob...
read it
-
Complete Dictionary Recovery over the Sphere
We consider the problem of recovering a complete (i.e., square and inver...
read it
-
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...
read it
-
Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
Recovering matrices from compressive and grossly corrupted observations ...
read it
-
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Recovering a low-rank tensor from incomplete information is a recurring ...
read it
-
Toward Guaranteed Illumination Models for Non-Convex Objects
Illumination variation remains a central challenge in object detection a...
read it
-
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...
read it
-
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...
read it
-
Sparsity and Robustness in Face Recognition
This report concerns the use of techniques for sparse signal representat...
read it