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researcher

Yunfei Yang

10 papers here

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

author position
  • first author4
  • middle author3
  • last author3

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CV1
  • eess.SP1
  • stat.ML1
ORCID 0000-0001-7231-4816
same name
  • Yunfei Yang — 8 papers, h 13
  • Yunfei Yang — 5 papers, h 2
  • Yunfei Yang — 4 papers, h 3
  • Yunfei Yang — 3 papers
  • Yunfei Yang — 1 paper, h 2
  • Yunfei Yang — 1 paper, h 3

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
20182023
most citedProgressive Weight Pruning of Deep Neural Networks using ADMM

37 citations · 135 across the 10 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021★ 1 cited

Non-Asymptotic Error Bounds for Bidirectional GANs

Shiao Liu, Yunfei Yang, Jian Huang +2

We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimation error under the Dudley distance between the latent joint distribution and the data joint distribution wit…

eess.SP2021

Exponential Approximation of Band-limited Functions from Nonuniform Sampling by Regularization Methods

Yunfei Yang, Haizhang Zhang

Reconstructing a band-limited function from its finite sample data is a fundamental task in signal analysis. A Gaussian regularized Shannon sampling series has been proved to be ab…

cs.LG2021★ 16 cited

An error analysis of generative adversarial networks for learning distributions

Jian Huang, Yuling Jiao, Zhen Li +3

This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples. Our main results establish the convergence rates of GANs und…

cs.LG2021★ 25 cited

On the capacity of deep generative networks for approximating distributions

Yunfei Yang, Zhen Li, Yang Wang

We study the efficacy and efficiency of deep generative networks for approximating probability distributions. We prove that neural networks can transform a low-dimensional source d…

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