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researcher

Jiayi Wang

4 papers here

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

author position
  • first author2
  • middle author2

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

fields
  • cs.LG2
  • cs.CL1
  • cs.CV1
ORCID 0000-0002-7785-3381
same name
  • Jiayi Wang — 7 papers, h 8
  • Jiayi Wang — 4 papers
  • Jiayi Wang — 4 papers
  • Jiayi Wang — 4 papers
  • Jiayi Wang — 2 papers, h 5
  • Jiayi Wang — 2 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

most citedFew-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation

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

collaborators

4 papers

cs.LG2024

A New Theoretical Perspective on Data Heterogeneity in Federated Optimization

Jiayi Wang, Shiqiang Wang, Rong-Rong Chen +1

In federated learning (FL), data heterogeneity is the main reason that existing theoretical analyses are pessimistic about the convergence rate. In particular, for many FL algorith…

cs.LG2024

MPRE: Multi-perspective Patient Representation Extractor for Disease Prediction

Ziyue Yu, Jiayi Wang, Wuman Luo +2

Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dyna…

cs.CL2022

Rethinking Textual Adversarial Defense for Pre-trained Language Models

Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang +1

Although pre-trained language models (PrLMs) have achieved significant success, recent studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating adversari…

cs.CV2022★ 4 cited

Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation

Hegui Zhu, Zhan Gao, Jiayi Wang +2

Traditional fine-grained image classification typically relies on large-scale training samples with annotated ground-truth. However, some sub-categories have few available samples…

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