3 papers
cs.LG2026
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
Boyang Fan, Hengchuang Yin, Siyu Yi +5
Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Existing graph-based methods optimiz…
cs.LG2025
Rewarding the Journey, Not Just the Destination: A Composite Path and Answer Self-Scoring Reward Mechanism for Test-Time Reinforcement Learning
Jingyu Xing, Chenwei Tang, Xinyu Liu +5
Reinforcement Learning (RL) has emerged as a powerful paradigm for advancing Large Language Models (LLMs), achieving remarkable performance in complex reasoning domains such as mat…
cs.LG2024
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation
Peidong Liu, Wenbo Zhang, Wei Ju +2
The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-encoders and diffusion models. How…