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Wen Huang

4 papers hereh-index 201.2k citations57 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.SI2
  • cs.IT1
  • math.OC1
same name
  • Wen Huang — 28 papers, h 15
  • Wen Huang — 16 papers, h 37
  • Wen Huang — 5 papers
  • Wen Huang — 5 papers
  • Wen Huang — 4 papers
  • Wen Huang — 4 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedA Provably Convergent Scheme for Compressive Sensing under Random Generative Priors

17 citations · 17 across the 3 of their papers we have counts for

collaborators

4 papers

cs.SI2020

Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem

Melissa Marchand, Kyle A. Gallivan, Wen Huang +1

In this paper we analyze an indirect approach, called the Neighborhood Pattern Similarity approach, to solve the so-called role extraction problem of a large-scale graph. The metho…

cs.SI2020

Community Detection by a Riemannian Projected Proximal Gradient Method

Meng Wei, Wen Huang, Kyle A. Gallivan +1

Community detection plays an important role in understanding and exploiting the structure of complex systems. Many algorithms have been developed for community detection using modu…

math.OC2018★ 17 cited

A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors

Wen Huang, Paul Hand, Reinhard Heckel +1

Deep generative modeling has led to new and state of the art approaches for enforcing structural priors in a variety of inverse problems. In contrast to priors given by sparsity, d…

cs.IT2018

Rate-Optimal Denoising with Deep Neural Networks

Reinhard Heckel, Wen Huang, Paul Hand +1

Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying princip…

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