报告题目：胶囊网络及其应用
报告时间：2018年6月7日（周四）下午1:30-5:30
报告地点：吉林大学南校区计算机大楼A521讲学厅
报告人： 许东教授
Department of Electrical Engineering and Computer Science
Christopher S. Bond Life Sciences Center
University of Missouri, Columbia, MO, 65201, USA
报告摘要：
To address the problems of scalar neurons, a novel deep-learning architecture, known as Capsule Network (CapsNet) was introduced in 2017. The main building block of CapsNet is the capsule, which is a group of neuron vectors whose lengths represent the entity probabilities. Capsule provides a unique and powerful deep-learning building block to better model the diverse relationships inside internal representations of a neural network. We have applied CapsNet in several applications and achieved improved performance over previous deep-learning methods. In this talk, I will discuss the concept, implementation and applications of CapsNet in details.
主办单位：
吉林大学计算机科学与技术学院
吉林大学软件学院
吉林大学计算机科学技术研究所
符号计算与知识工程教育部重点实验室
