3 citations · 4 across the 3 of their papers we have counts for
17 papers
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
Chengsheng Mao, Liang Yao, Yuan Luo
Acute kidney injury (AKI) is a common clinical syndrome characterized by a sudden episode of kidney failure or kidney damage within a few hours or a few days. Accurate early predic…
Distribution Preserving Graph Representation Learning
Chengsheng Mao, Yuan Luo
Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted gr…
Towards Expressive Graph Representation
Chengsheng Mao, Liang Yao, Yuan Luo
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most exist…
KG-BERT: BERT for Knowledge Graph Completion
Liang Yao, Chengsheng Mao, Yuan Luo
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models fo…
Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model
Chengsheng Mao, Lijuan Lu, Bin Hu
In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distri…
Local Distribution in Neighborhood for Classification
Chengsheng Mao, Bin Hu, Lei Chen +2
The k-nearest-neighbor method performs classification tasks for a query sample based on the information contained in its neighborhood. Previous studies into the k-nearest-neighbor…