306 citations · 1.5k across the 30 of their papers we have counts for
31 papers
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminati…
Deep Networks from the Principle of Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gr…
Deep Networks and the Multiple Manifold Problem
Sam Buchanan, Dar Gilboa, John Wright
We study the multiple manifold problem, a binary classification task modeled on applications in machine vision, in which a deep fully-connected neural network is trained to separat…
From Symmetry to Geometry: Tractable Nonconvex Problems
Yuqian Zhang, Qing Qu, John Wright
As science and engineering have become increasingly data-driven, the role of optimization has expanded to touch almost every stage of the data analysis pipeline, from signal and da…
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications
Qing Qu, Zhihui Zhu, Xiao Li +3
The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applicat…
Short-and-Sparse Deconvolution -- A Geometric Approach
Yenson Lau, Qing Qu, Han-Wen Kuo +3
Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure. Variants of this problem arise in appl…