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Junfeng Fang

45 papers hereh-index 14689 citations50 works total

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

author position
  • first author4
  • middle author36
  • last author1

Across the 41 of 45 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • cs.CL15
  • cs.AI5
  • cs.CV4
  • cs.CR2
  • cs.IR2
same name
  • Junfeng Fang — 16 papers, h 15
  • Junfeng Fang — 12 papers, h 3
  • Junfeng Fang — 8 papers, h 6
  • Junfeng Fang — 7 papers, h 2
  • Junfeng Fang — 2 papers, h 1
  • Junfeng Fang — 1 paper

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
20242026
most citedNExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation

2 citations · 5 across the 41 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CL2024★ 1 cited

Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence

Jinghan He, Kuan Zhu, Haiyun Guo +6

Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite…

cs.CL2024

Neuron-Level Sequential Editing for Large Language Models

Houcheng Jiang, Junfeng Fang, Tianyu Zhang +4

This work explores sequential model editing in large language models (LLMs), a critical task that involves modifying internal knowledge within LLMs continuously through multi-round…

cs.LG2024

Text-guided Diffusion Model for 3D Molecule Generation

Yanchen Luo, Junfeng Fang, Sihang Li +5

The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property va…

cs.LG2024★ 1 cited

DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector

Jinghan Li, Yuan Gao, Jinda Lu +4

Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods,…

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