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Han Huang

The Chinese University of Hong Kong

9 papers hereh-index 8404 citations11 works total

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

author position
  • first author4
  • middle author4

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • q-bio.BM3
  • cs.IR1
  • q-bio.QM1
affiliations
  • The Chinese University of Hong Kong
same name
  • Han Huang — 14 papers, h 7
  • Han Huang — 14 papers, h 7
  • Han Huang — 7 papers, h 1
  • Han Huang — 6 papers, h 2
  • Han Huang — 4 papers, h 8
  • Han Huang — 4 papers, h 6

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
20222026
most citedLearning Joint 2D & 3D Diffusion Models for Complete Molecule Generation

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

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Scaling RNA Foundation Models through Iterative Refinement

Pengan Chen, Liang Hong, Xinyuan Liu +6

Biological function in RNA is encoded across scales, from local sequence motifs to long-range structural dependencies. Conventional RNA foundation models couple parameter capacity…

cs.LG2023

PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation

Mingzhe Liu, Han Huang, Hao Feng +3

Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in…

cs.LG2023★ 3 cited

Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

Han Huang, Leilei Sun, Bowen Du +1

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, acc…

cs.LG2022★ 2 cited

GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation

Han Huang, Leilei Sun, Bowen Du +2

Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due t…

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