中国科学院自动化研究所   设为首页   加入收藏  联系我们
 
English
网站首页     实验室概况     研究队伍     组织机构     学术交流     科研成果     人才培养     开放课题     创新文化     资源共享     联系我们
    学术讲座

On New Connections between Deep Learning and The Theory of Belief Functions

模式识别学术大讲堂
Advanced Lecture Series in Pattern Recognition
题     目 (TITLE):On New Connections between Deep Learning and The Theory of Belief Functions
讲 座 人 (SPEAKER):Prof. Thierry Denoeux
主 持 人 (CHAIR):Prof. Chenglin Liu 
时     间 (TIME):10:30 am, Nov. 12 (Tuesday), 2019
地     点 (VENUE):No.1 Conference Room (3rd floor), Intelligence Building
报告摘要(ABSTRACT):

The Dempster-Shafer theory of belief functions is a formal framework for modeling and reasoning with uncertainty. It is based on the representation of independent pieces of evidence by belief functions, and on their combination by an operator called Dempster’s rule. In this talk, I show that the weighted sum and softmax operations performed in the output layer of feedforward neural networks can be interpreted in terms of evidence aggregation using Dempster's rule of combination. From that perspective, the output probabilities computed by such classifiers can be seen as being derived from some belief functions, which can be laid bare and used for decision making or classifier fusion. The same analysis pertains to other classifiers such as Support Vector Machines. This finding suggests that the links between machine learning and belief functions are closer than is usually assumed, and that Dempster-Shafer theory provides a suitable framework for developing new machine learning algorithms.
报告人简介(BIOGRAPHY):

Thierry Denoeux is a Full Professor (Exceptional Class) with the Department of Information Processing Engineering at Université de Technologie de Compiègne, France. He is the director of the Laboratory of Excellence on “Technological Systems of Systems” and the president of the  Belief Functions and Applications Society. In 2019, he was appointed as a senior member of Institut Universitaire de France. His research interests concern reasoning and decision-making under uncertainty and, more generally, the management of uncertainty in intelligent systems. His main contributions are in the theory of belief functions with applications to statistical inference, pattern recognition, machine learning and information fusion. He is the author of more than 200 papers in journals and conference proceedings and he has supervised more than 30 PhD theses. He is the Editor-in-Chief of two Elsevier journals: 'International Journal of Approximate Reasoning' and ‘Array', and an Associate Editor of several journals including 'Fuzzy Sets and Systems' and 'International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems'.

友情链接
 
中科院自动化研究所 模式识别国家重点实验室
NLPR, INSTITUTE OF AUTOMATION, CHNESE ACADEMY OF SCIENCES