The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

Vision-guided learning for autonomous robotic manipulations is a wide-ranging and high-
impact topic in the context of smart manufacturing. Most learning strategies are object …

In-situ analytics has lately been shown to be an effective approach to reduce both I/O and
storage costs for scientific analytics. Developing an efficient in-situ implementation, however …

With several characteristics, such as large scale, diverse predictability and timeliness, the
city traffic data falls in the range of definition of Big Data. A Virtual Reality GIS based traffic …

Abstract Sign Language Recognition (SLR) has become one of the most important research
areas in the field of human computer interaction. SLR systems are meant to automatically …

As an important part of traditional medicine, TCM (Traditional Chinese Medicine) has unique
and distinct clinical effects in the aspect of disease diagnosis and treatment. Thousands of …

Context of data points, which is usually defined as the other data points in a data set, has
been found to paly important roles in data representation and classification. In this paper, we …

In this paper we study the problem of learning from multiple modal data for purpose of
document classification. In this problem, each document is composed two different modals of …

针对工业过程监控中传统主元分析(PCA) 方法没有突出局部变量信息的问题,
提出一种基于变量子域PCA (variable sub-region PCA, VSR-PCA) 的故障检测方法 …

In this paper we investigate the usage of regularized correntropy framework for learning of
classifiers from noisy labels. The class label predictors learned by minimizing transitional …