6 papers
SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Large scale pretrained models have revolutionized Natural Language Processing (NLP) and Computer Vision (CV), showcasing remarkable cross domain generalization abilities. However,…
Multi-Modal Molecular Representation Learning via Structure Awareness
Rong Yin, Ruyue Liu, Xiaoshuai Hao +4
Accurate extraction of molecular representations is a critical step in the drug discovery process. In recent years, significant progress has been made in molecular representation l…
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning
Ruyue Liu, Rong Yin, Yong Liu +4
Graph Contrastive Learning (GCL) has emerged as the foremost approach for self-supervised learning on graph-structured data. GCL reduces reliance on labeled data by learning robust…
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized…
Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation
Xunyu Zhu, Jian Li, Can Ma +1
Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational…
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model
Xunyu Zhu, Jian Li, Can Ma +1
Large Language Models (LLMs) have demonstrated exceptional proficiency in mathematical reasoning tasks due to their extensive parameter counts and training on vast datasets. Despit…