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Hongwei Jin

15 papers hereh-index 13697 citations69 works total

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

author position
  • first author10
  • middle author4
  • last author1

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • math.RA3
  • cs.DC2
  • math.NA2
  • cs.AI1
  • cs.SE1
same name
  • Hongwei Jin — 3 papers, h 34
  • Hongwei Jin — 3 papers, h 7

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
20152025
most citedFlow-Bench: A Dataset for Computational Workflow Anomaly Detection

3 citations · 14 across the 15 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

math.RA2022★ 2 cited

The generalized inverses of the quaternion tensor via the T-product

Hongwei Jin, Peifeng Zhou, Hongjie Jiang +1

In this article, specific definitions of the Moore-Penrose inverse, Drazin inverse of the quaternion tensor and the inverse along two quaternion tensors are introduced under the T-…

math.RA2022★ 1 cited

The generalized inverses of tensors via the C-Product

Hongwei Jin, Shumin Xu, Hongjie Jiang +1

This paper studies the issues about the generalized inverses of tensors under the C-Product. The aim of this paper is threefold. Firstly, this paper present the definition of the M…

cs.LG2022

Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound

Hongwei Jin, Zishun Yu, Xinhua Zhang

Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wass…

cs.LG2022★ 2 cited

Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs

Hongwei Jin, Xun Chen

Learning the similarity between structured data, especially the graphs, is one of the essential problems. Besides the approach like graph kernels, Gromov-Wasserstein (GW) distance…

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