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Ying Wang

4 papers hereh-index 322 citations4 works total

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

author position
  • middle author4

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

fields
  • cs.LG4
same name
  • Ying Wang — 17 papers
  • Ying Wang — 12 papers, h 3
  • Ying Wang — 11 papers, h 5
  • Ying Wang — 10 papers, h 10
  • Ying Wang — 10 papers, h 3
  • Ying Wang — 9 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

most citedEfficient Sharpness-Aware Minimization for Molecular Graph Transformer Models

2 citations · 2 across the 2 of their papers we have counts for

collaborators

4 papers

cs.LG2025

CrystalICL: Enabling In-Context Learning for Crystal Generation

Ruobing Wang, Qiaoyu Tan, Yili Wang +2

Designing crystal materials with desired physicochemical properties remains a fundamental challenge in materials science. While large language models (LLMs) have demonstrated stron…

cs.LG2024★ 2 cited

Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models

Yili Wang, Kaixiong Zhou, Ninghao Liu +2

Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and m…

cs.LG2024

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark

Yili Wang, Yixin Liu, Xu Shen +6

To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD)…

cs.LG2024

Rethinking Independent Cross-Entropy Loss For Graph-Structured Data

Rui Miao, Kaixiong Zhou, Yili Wang +3

Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node…

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