activity
20182023
most citedExploring Large Language Models for Knowledge Graph Completion

24 citations · 36 across the 8 of their papers we have counts for

collaborators
Showing 2018Show all

13 papers · 1 filter

cs.LG2018★ 1 cited

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…

cs.LG2018

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…

cs.LG2018

Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes

Yikuan Li, Liang Yao, Chengsheng Mao +3

Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will…

cs.LG2018

Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk

Guoqing Chao, Chengsheng Mao, Fei Wang +2

ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpr…

q-bio.GN2018

Cancer classification and pathway discovery using non-negative matrix factorization

Zexian Zeng, Andy Vo, Chengsheng Mao +3

Extracting genetic information from a full range of sequencing data is important for understanding diseases. We propose a novel method to effectively explore the landscape of genet…

cs.LG2018

Distribution Networks for Open Set Learning

Chengsheng Mao, Liang Yao, Yuan Luo

In open set learning, a model must be able to generalize to novel classes when it encounters a sample that does not belong to any of the classes it has seen before. Open set learni…