Yaming Yang

Interpretable and Efficient Heterogeneous Graph Convolutional Network

2021 · IEEE Transactions on Knowledge and Data Engineering

Graph Convolutional Network (GCN) has achieved extraordinary success in learning representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore …

Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees

2021 · arXiv (Cornell University)

Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information has been proved to be crucial for the success of NLP applications. However, how …

Aligning Multiple Knowledge Graphs in A Single Pass

2026

Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, …

MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning

2024 · arXiv (Cornell University)

Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness. However, in multi-task learning (MTL) scenarios, LoRA tends to obscure …

RGCNet: Riemannian graph convolutional networks for end-to-end smart contract vulnerability detection

2026 · Neurocomputing

Frequent security issues with smart contract vulnerabilities have become a pressing challenge in the industry. Conventional program analysis methods lack flexibility and extensibility, leading to high false positive rates. Deep learning approaches are emerging as …