Good Papers

Showing papers from The Hong Kong University of Science and Technology (Guangzhou Campus) Show all papers

57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026SUN YAT-SEN UNIVERSITYTencent AI for Life Sciences LabTencentTsinghua University, TsinghuaThe Hong Kong University of ScieGenomics & single-cell

WaveSem: Frequency-Adaptive Tokenization for Disentangling Semantics and Noise in Genomics

Shou Z Chen, Bing He, Zhenchao Tang, Jun Zhu and 8 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026WestlakeTencent AI LabTencentTencent AI for Life Sciences LabThe Hong Kong University of ScieGenomics & single-cell

UGM: Unified Multi-scale Genomic Event Modeling with Site-level Joint Prediction

Jiayang Wu, Chenchen Qin, Xu YANG, Yu Zhao and 7 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
89%Must read
?Must readVote to see the score
NeurIPS 2026TencentHarbin Institute of TechnologyHong Kong PolytechnicWestlakeJiangnanAlignment & preference optimization

Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning

Balanced Fine-Tuning uses dual-scale token and sequence reweighting targeting dense epistemic uncertainty to align LLMs with biomedical knowledge, improving reasoning and sparse-reward RL over standard fine-tuning.

Zhenchao Tang, Fang Wang, Haohuai He, Jiale Zhou and 12 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
16/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5