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Tianyi Zhou

University of Maryland

38 papers hereh-index 336k citations83 works total

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

author position
  • first author9
  • middle author27
  • last author1

Across the 37 of 38 papers where every author was matched, so the position is known.

fields
  • cs.LG18
  • cs.CL12
  • stat.ML4
  • cs.CV3
  • cs.IT1
affiliations
  • University of Maryland
  • University of Washington
  • University of Technology, Sydney
  • Nanyang Technological University
Homepage
same name
  • Tianyi Zhou — 33 papers, h 11
  • Tianyi Zhou — 14 papers, h 16
  • Tianyi Zhou — 13 papers, h 11
  • Tianyi Zhou — 11 papers, h 9
  • Tianyi Zhou — 11 papers, h 10
  • Tianyi Zhou — 10 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
20102023
most citedMulti-Center Federated Learning: Clients Clustering for Better Personalization

300 citations · 1.5k across the 38 of their papers we have counts for

collaborators
Showing 2019Show all

3 papers · 1 filter

cs.CL2019★ 7 cited

Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

Yang Li, Guodong Long, Tao Shen +4

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention m…

cs.LG2019★ 49 cited

Learning to Propagate for Graph Meta-Learning

Lu Liu, Tianyi Zhou, Guodong Long +2

Meta-learning extracts common knowledge from learning different tasks and uses it for unseen tasks. It can significantly improve tasks that suffer from insufficient training data,…

cs.LG2019★ 5 cited

Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

Lu Liu, Tianyi Zhou, Guodong Long +3

A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heav…

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