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Ziyang Zhang

4 papers hereh-index 11338 citations33 works total

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

author position
  • first author1
  • middle author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.CV1
  • cs.SI1
same name
  • Ziyang Zhang — 11 papers, h 12
  • Ziyang Zhang — 10 papers
  • Ziyang Zhang — 9 papers, h 6
  • Ziyang Zhang — 7 papers, h 5
  • Ziyang Zhang — 6 papers, h 2
  • Ziyang Zhang — 5 papers, 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

most citedAn Interpretable Deep Semantic Segmentation Method for Earth Observation

1 citations · 3 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2023★ 1 cited

Koopman neural operator as a mesh-free solver of non-linear partial differential equations

Wei Xiong, Xiaomeng Huang, Ziyang Zhang +3

The lacking of analytic solutions of diverse partial differential equations (PDEs) gives birth to a series of computational techniques for numerical solutions. Although numerous la…

cs.LG2023★ 1 cited

KoopmanLab: machine learning for solving complex physics equations

Wei Xiong, Muyuan Ma, Xiaomeng Huang +3

Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions…

cs.CV2022★ 1 cited

An Interpretable Deep Semantic Segmentation Method for Earth Observation

Ziyang Zhang, Plamen Angelov, Eduardo Soares +2

Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers. Semantic segmentation plays a key role…

cs.SI2022

Network comparison via encoding, decoding, and causality

Yang Tian, Hedong Hou, Guangzheng Xu +2

Quantifying the relations (e.g., similarity) between complex networks paves the way for studying the latent information shared across networks. However, fundamental relation metric…

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