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researcher

Junzhou Huang

115 papers hereh-index 8530.6k citations397 works total

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

author position
  • first author2
  • middle author54
  • last author59

Across the 115 of 115 papers where every author was matched, so the position is known.

fields
  • cs.LG56
  • cs.CV34
  • cs.SI6
  • eess.IV6
  • cs.CL2
  • cs.DB2
same name
  • Junzhou Huang — 13 papers, h 6
  • Junzhou Huang — 6 papers, h 3
  • Junzhou Huang — 5 papers, h 5
  • Junzhou Huang — 3 papers
  • Junzhou Huang — 3 papers, h 3
  • Junzhou Huang — 2 papers, h 2

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
20092023
most citedDropEdge: Towards Deep Graph Convolutional Networks on Node Classification

649 citations · 4.6k across the 115 of their papers we have counts for

collaborators
Showing 2017 · cs.LGShow all

4 papers · 2 filters

cs.LG2017★ 1 cited

Robust Contextual Bandit via the Capped-ℓ2​ norm

Feiyun Zhu, Xinliang Zhu, Sheng Wang +2

This paper considers the actor-critic contextual bandit for the mobile health (mHealth) intervention. The state-of-the-art decision-making methods in mHealth generally assume that…

cs.LG2017★ 5 cited

Group-driven Reinforcement Learning for Personalized mHealth Intervention

Feiyun Zhu, Jun Guo, Zheng Xu +2

Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health. State-…

cs.LG2017★ 9 cited

Learning Graph While Training: An Evolving Graph Convolutional Neural Network

Ruoyu Li, Junzhou Huang

Convolution Neural Networks on Graphs are important generalization and extension of classical CNNs. While previous works generally assumed that the graph structures of samples are…

cs.LG2017★ 4 cited

Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention

Feiyun Zhu, Peng Liao, Xinliang Zhu +2

In the wake of the vast population of smart device users worldwide, mobile health (mHealth) technologies are hopeful to generate positive and wide influence on people's health. The…

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