activity
20142023
most citedGraph Neural Networks: A Review of Methods and Applications

1.4k citations · 5.4k across the 120 of their papers we have counts for

collaborators
Showing cs.AIShow all

8 papers · 1 filter

cs.AI2023

Automatic Truss Design with Reinforcement Learning

Weihua Du, Jinglun Zhao, Chao Yu +7

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal la…

cs.AI2021★ 15 cited

Pre-Trained Models: Past, Present and Future

Xu Han, Zhengyan Zhang, Ning Ding +21

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophis…

cs.AI2021

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability

Xin Lv, Yixin Cao, Lei Hou +4

Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are…

cs.AI2019★ 25 cited

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations

Xin Lv, Yuxian Gu, Xu Han +3

Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous met…

cs.AI2019

Quantifying Similarity between Relations with Fact Distribution

Weize Chen, Hao Zhu, Xu Han +2

We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence betwe…

cs.AI2018★ 8 cited

Differentiating Concepts and Instances for Knowledge Graph Embedding

Xin Lv, Lei Hou, Juanzi Li +1

Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding met…

cs.AI2018★ 21 cited

Overview of CAIL2018: Legal Judgment Prediction Competition

Haoxi Zhong, Chaojun Xiao, Zhipeng Guo +8

In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to pred…

cs.AI2018★ 14 cited

Automatic Judgment Prediction via Legal Reading Comprehension

Shangbang Long, Cunchao Tu, Zhiyuan Liu +1

Automatic judgment prediction aims to predict the judicial results based on case materials. It has been studied for several decades mainly by lawyers and judges, considered as a no…