Recent advances in attention networks have gained enormous interest in time series data
mining. Various attention mechanisms are proposed to soft-select relevant timestamps from …

Utilizing multiple modalities to learn a good distance metric is of vital importance for various
clinical applications. However, it is common that modalities are incomplete for some patients …

Pairwise learning has received much attention recently as it is more capable of modeling the
relative relationship between pairs of samples. Many machine learning tasks can be …

Multitask learning (MTL) aims at solving the related tasks simultaneously by exploiting
shared knowledge to improve performance on individual tasks. Though numerous empirical …

Patient similarity search is an essential task in healthcare. Recent studies adopted electronic
health records (EHRs) to learn patient representations for measuring the clinical similarities …

Diagnosis prediction aims to predict the future health status of patients according to their
historical visit records, which is an important yet challenging task in healthcare informatics …

Predicting patients' future outcomes by analyzing Electronic health records (EHRs) is a hot
topic in machine learning. The key challenge in this area is how to transform high …

Various biometrics such as the face, irises, and fingerprints, which can be obtained in a
relatively simple way in modern society, are used in personal authentication systems to …

Instead of learning with pointwise loss functions, learning with pairwise loss functions
(pairwise learning) has received much attention recently as it is more capable of modeling …

As an effective way to learn a distance metric between pairs of samples, deep metric
learning (DML) has drawn significant attention in recent years. The key idea of DML is to …