5 citations · 11 across the 6 of their papers we have counts for
6 papers
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Hanxuan Yang, Zhaoxin Yu, Qingchao Kong +2
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires…
YAYI 2: Multilingual Open-Source Large Language Models
Yin Luo, Qingchao Kong, Nan Xu +50
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world…
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning
Hanxuan Yang, Qingchao Kong, Wenji Mao
Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. I…
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
Xinglin Xiao, Yijie Wang, Nan Xu +7
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures. Recent work has proposed methods based on…
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning
Hanxuan Yang, Ruike Zhang, Qingchao Kong +1
Graph representation learning is a fundamental research issue and benefits a wide range of applications on graph-structured data. Conventional artificial neural network-based metho…
A Deep Latent Space Model for Graph Representation Learning
Hanxuan Yang, Qingchao Kong, Wenji Mao
Graph representation learning is a fundamental problem for modeling relational data and benefits a number of downstream applications. Traditional Bayesian-based graph models and re…