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Zhao Zhang

Hefei University of Technology

11 papers hereh-index 384.8k citations238 works total

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

author position
  • first author2
  • middle author9

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • cs.LG4
  • astro-ph.HE1
  • cs.DB1
affiliations
  • Hefei University of Technology
same name
  • Zhao Zhang — 27 papers, h 27
  • Zhao Zhang — 17 papers, h 17
  • Zhao Zhang — 15 papers
  • Zhao Zhang — 15 papers, h 10
  • Zhao Zhang — 12 papers, h 12
  • Zhao Zhang — 10 papers, h 12

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
20192026
most citedA Survey on Incomplete Multi-view Clustering

248 citations · 278 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 248 cited

A Survey on Incomplete Multi-view Clustering

Jie Wen, Zheng Zhang, Lunke Fei +4

Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, suc…

cs.LG2021★ 9 cited

A Simple Approach to Automated Spectral Clustering

Jicong Fan, Yiheng Tu, Zhao Zhang +2

The performance of spectral clustering heavily relies on the quality of affinity matrix. A variety of affinity-matrix-construction (AMC) methods have been proposed but they have hy…

cs.LG2020★ 3 cited

Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis

Jicong Fan, Lijun Ding, Chengrun Yang +2

The nuclear norm and Schatten-p quasi-norm are popular rank proxies in low-rank matrix recovery. However, computing the nuclear norm or Schatten-p quasi-norm of a tensor is har…

cs.LG2020

Convolutional Neural Network Training with Distributed K-FAC

J. Gregory Pauloski, Zhao Zhang, Lei Huang +2

Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales. The Kronecker-factored…

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