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researcher

Jun Yu

26 papers here

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

author position
  • first author6
  • middle author11
  • last author5

Across the 22 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CV18
  • cs.LG3
  • cs.AI2
  • eess.IV1
  • math.ST1
  • quant-ph1
ORCID 0000-0003-1922-7283
same name
  • Jun Yu — 23 papers, h 42
  • Jun Yu — 12 papers, h 8
  • Jun Yu — 12 papers
  • Jun Yu — 11 papers, h 6
  • Jun Yu — 7 papers
  • Jun Yu — 6 papers

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
20162025
most citedA Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

156 citations · 218 across the 26 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022★ 1 cited

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

Jun Yu, Zhaoming Kong, Liang Zhan +2

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combi…

cs.CV2022★ 3 cited

Learning Disentangled Representations for Controllable Human Motion Prediction

Chunzhi Gu, Jun Yu, Chao Zhang

Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lo…

cs.CV2022

Hyper-relationship Learning Network for Scene Graph Generation

Yibing Zhan, Zhi Chen, Jun Yu +3

Generating informative scene graphs from images requires integrating and reasoning from various graph components, i.e., objects and relationships. However, current scene graph gene…

cs.LG2022

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Yexiong Lin, Yu Yao, Yuxuan Du +4

Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels. Intuitively, when there is a finite training sample, penalizing the variance of…

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