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Yang Wang

19 papers hereh-index 13521 citations21 works total

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

author position
  • sole author2
  • middle author10
  • last author6

Across the 18 of 19 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • math.NA5
  • cs.CV1
  • cs.HC1
  • cs.IT1
  • cs.NE1
same name
  • Yang Wang — 91 papers, h 102
  • Yang Wang — 34 papers, h 24
  • Yang Wang — 20 papers, h 14
  • Yang Wang — 15 papers, h 3
  • Yang Wang — 14 papers, h 6
  • Yang Wang — 12 papers, h 10

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

activity
20152023
most citedAdvbox: a toolbox to generate adversarial examples that fool neural networks

40 citations · 171 across the 18 of their papers we have counts for

collaborators
Showing 2021 · math.NAShow all

4 papers · 2 filters

math.NA2021

The global landscape of phase retrieval II: quotient intensity models

Jian-Feng Cai, Meng Huang, Dong Li +1

A fundamental problem in phase retrieval is to reconstruct an unknown signal from a set of magnitude-only measurements. In this work we introduce three novel quotient intensity-bas…

math.NA2021

The global landscape of phase retrieval I: perturbed amplitude models

Jian-Feng Cai, Meng Huang, Dong Li +1

A fundamental task in phase retrieval is to recover an unknown signal $\vx\in \Rn$ from a set of magnitude-only measurements $y_i=\abs{\nj{\va_i,\vx}}, \; i=1,\ldots,m$. In this pa…

math.NA2021★ 3 cited

Linear convergence of randomized Kaczmarz method for solving complex-valued phaseless equations

Meng Huang, Yang Wang

A randomized Kaczmarz method was recently proposed for phase retrieval, which has been shown numerically to exhibit empirical performance over other state-of-the-art phase retrieva…

math.NA2021

Error Analysis of Deep Ritz Methods for Elliptic Equations

Yuling Jiao, Yanming Lai, Yisu Lo +2

Using deep neural networks to solve PDEs has attracted a lot of attentions recently. However, why the deep learning method works is falling far behind its empirical success. In thi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.