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researcher

Licheng Jiao

34 papers hereh-index 16946 citations135 works total

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

author position
  • middle author28
  • last author4

Across the 32 of 34 papers where every author was matched, so the position is known.

fields
  • cs.CV28
  • cs.NE4
  • cs.AI1
  • eess.IV1
same name
  • Licheng Jiao — 14 papers, h 43
  • Licheng Jiao — 12 papers, h 9
  • Licheng Jiao — 12 papers, h 6
  • Licheng Jiao — 8 papers, h 10
  • Licheng Jiao — 3 papers, h 4
  • Licheng Jiao — 3 papers, h 5

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
20152026
most citedAIM 2024 Sparse Neural Rendering Challenge: Methods and Results

8 citations · 13 across the 31 of their papers we have counts for

collaborators
Showing cs.NEShow all

4 papers · 1 filter

cs.NE2026

Task-free Adaptive Meta Black-box Optimization

Chao Wang, Licheng Jiao, Lingling Li +4

Handcrafted optimizers become prohibitively inefficient for complex black-box optimization (BBO) tasks. MetaBBO addresses this challenge by meta-learning to automatically configure…

cs.NE2025

Learning Evolution via Optimization Knowledge Adaptation

Chao Wang, Lingling Li, Licheng Jiao +3

The iterative search process of evolutionary algorithms (EAs) encapsulates optimization knowledge within historical populations and fitness evaluations. Effective utilization of th…

cs.NE2024

Knowledge-aware Evolutionary Graph Neural Architecture Search

Chao Wang, Jiaxuan Zhao, Lingling Li +4

Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin…

cs.NE2024

Automatic Graph Topology-Aware Transformer

Chao Wang, Jiaxuan Zhao, Lingling Li +3

Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities. How…

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