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researcher

Rong Zhang

4 papers hereh-index 213 citations6 works total

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

author position
  • first author1
  • middle author3

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

fields
  • cs.LG2
  • cs.AI1
  • q-bio.QM1
same name
  • Rong Zhang — 19 papers, h 13
  • Rong Zhang — 17 papers, h 51
  • Rong Zhang — 9 papers, h 6
  • Rong Zhang — 9 papers, h 8
  • Rong Zhang — 8 papers, h 12
  • Rong Zhang — 6 papers, h 33

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 citedPointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2026

CliffRank: A Dual-Branch Framework for Activity-Cliff Ranking Prediction

Kewei Li, Rongying Zhang, Peiyu Yang +4

Activity-cliff ranking remains difficult because local structural changes can cause large activity differences, while high-quality data that resolve the underlying mechanisms remai…

cs.AI2026

FLaG: Frequency-Domain Latent-attention Gated Pooling for Token Aggregation

Kewei Li, Rongying Zhang, Xueli Wang +6

Token aggregation converts token-level representations into fixed-dimensional sample representations, but most pooling methods operate only in the original token space. We introduc…

cs.LG2026

Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation

Kewei Li, Rongying Zhang, Xueli Wang +5

Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain. W…

q-bio.QM2023★ 1 cited

PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry

Rong Zhang, Rongqing Yuan, Boxue Tian

Predicting quantum chemical properties is a fundamental challenge for computational chemistry. While the development of graph neural networks has advanced molecular representation…

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