37 citations · 55 across the 5 of their papers we have counts for
6 papers
Learning Time Series from Scale Information
Yuan Yang, Jie Ding
Sequentially obtained dataset usually exhibits different behavior at different data resolutions/scales. Instead of inferring from data at each scale individually, it is often more…
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
Yuyu Zhang, Xinshi Chen, Yuan Yang +4
Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in ML…
Learn to Explain Efficiently via Neural Logic Inductive Learning
Yuan Yang, Le Song
The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to expla…
Can Graph Neural Networks Help Logic Reasoning?
Yuyu Zhang, Xinshi Chen, Yuan Yang +4
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training da…
Predicting Discharge Medications at Admission Time Based on Deep Learning
Yuan Yang, Pengtao Xie, Xin Gao +4
Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen…
CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System
Yuan Yang, Jingcheng Yu, Ye Hu +2
In this paper, we present LiveMedQA, a question answering system that is optimized for consumer health question. On top of the general QA system pipeline, we introduce several new…