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【学术报告】:Nikhil R Pal院士学术报告通知:用于可视化、系统识别和流形学习结构保持的降维模糊方法
发布人:赵振华  发布时间:2022-07-15   浏览次数:10

中文版:

题目:Nikhil R Pal院士学术报告通知:用于可视化、系统识别和流形学习结构保持的降维模糊方法

 

报告人Nikhil R Pal院士

报告题目:用于可视化、系统识别和流形学习结构保持的降维模糊方法

报告时间2022716日(周六)上午10:30

报告链接

内容摘要

  在这个大数据/人工智能/机器学习时代,当我们用约1750亿个自由参数训练网络时,降维有多重要?即使如今所有问题都不是大数据问题,不涉及文字和(或)图像,或很多问题不能依赖于黑盒模型,降维仍具有相关性和重要性。此外,通常这些黑盒系统无法应对现实生活中决策系统的开放世界性质。因此,降维、数据可视化、决策系统的透明度及其应对开放世界问题的能力非常重要。🍰下面将讨论模糊系统对于这些问题的方法设计:降维保持数据的几何结构;一种独特的综合特征选择和系统识别的方法;当高维数据基本上位于低维流形上时的一种流形学习方法,其中一些方法是首次尝试。接下来将阐述这样的模糊系统如何能够轻松地避免在它应该做出决定的时候做出决定。最后,将讨论一些需要进一步研究的问题。

 

个人简介

  Nikhil R. Pal是电子和通信科学部的教授,也是印度统计研究所人工智能和机器学习中心的负责人。他目前的研究方向包括脑科学、计算智能、机器学习和数据挖掘。20051月至201012月期间,他担任IEEE Transactions on Fuzzy Systems的主编。他曾/一直在多个期刊的编辑/顾问委员会/指导委员会任职,包括International Journal of Approximate Reasoning,Applied Soft Computing,Interna🎐tional Journal of Neural Systems, Fuzzy🔥 Sets and Systems, IEEE Transactions on Fuzzy SystemsIEEE Transactions on Cybernetics。他是2015IEEE计算智能学会(CIS)模糊系统先锋奖和2021IEEE CIS卓越服务奖的获得者。他在计算智能领域的不同主要国际会议上发表了许多全体会议/主题演讲。他曾担任多个会议的总主席、项目主席和联合项目主席。他曾是IEEE CIS的杰出讲师(2010-20122016-20182022-2024),并且是IEEE CIS管理委员会的成员(2010-2012)。他曾担任IEEE CIS出版副总裁(2013-2016)和IEEE CIS主席(2018-2019)。他是印度国家科学院、印度国家工程院、印度国家科学院、国际模糊系统协会(IFSA)、世界西孟加拉邦科学技术学院、电子和远程通信工程师学会研究员,也是美国IEEE的会员。 

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【编辑:王健】

 

英文版:

题目:Academic Report Notice of Nikhil R PalFuzzy approaches to structure🍌 preserving dimensionality reduction for visualization, systems identౠification, and manifold learning

 

Speaker: Academician Nikhil R Pal


Title: Fuzzy approaches 🔜to structure preserving dimensionality reduction for visualization,  sy🧜stems identification, and manifold learning 

Time: 10:30 am, July 16, 2022 (Saturday)

Link: 

 

Abstract:

      How relevant is dimensionality reduction in this era of big data/AI/ML when we train networks with about 175 billion free parameters? In my view, it𝔉 is still relevant and important because even today all problems are not big data problems; all problems do not involve texts and/or images;  and many problems cannot rely on black-box models. Moreover, usually these black-box systems cannot deal with the open-world nature of real-life decision making systems. Consequently, dimensionality reduction, data visualization, transparency of  a decision making system, and its ability to cope with the open-world problems are important. We shall discuss these issues and explain how fuzzy systems can realize the desirable attributes of a decision making system. Next I shall discuss &🎐nbsp;designing of fuzzy systems for these tasks: dimensionality reduction preserving the geometric structure of the data; an unique integrated approach to feature selection and system identification; and manifold learning when a high dimensional data lie essentially on a lower dimensional manifold. Some of these are first attempts. I shall demonstrate how easily such a system can refrain from making a decision when it should. Finally, I shall discuss some of the problems that need further investigation. 

 

Personal Introduction: 

      Nikhil R. Pal is a Professor in the Electronics and Communication Sciences Unit  and is the Head of the Center for Artificial Intelligence and Machine Learning of the Indian Statistical Institute. His current research interest includes brain science, computational intelligence, machine learning and data mining. He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems for the period January 2005-December 2010. He has served/been serving on the editorial /advisory board/ steering committee  of several  journals including the International Journal of Approximate Reasoning, Applied Soft Computing, International Journal of Neural Systems, Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Cybernetics. He is a recipient of the 2015 IEEE Computational Intelligence Society (CIS) Fuzzy Systems Pioneer Award and 2021 IEEE CIS Meritorious Service Award. He has given many plenary/keynote speeches in ༺different premier international conferences in the area of computational intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences. He has been a Distinguished Lecturer of the IEEE  CIS  (2010-2012, 2016-2018, 2022-2024) and was a member of the Administrative Committee of the IEEE CIS (2010-2012). He has served as the Vice-President for Publications of the IEEE CIS (2013-2016) and the President  of the IEEE CIS (2018-2019). He is a Fellow of the West Bengal Academy of Science and Technology, Institutio𝔍n of Electronics and Tele Communication Engineers, National Academy of Sciences, India, Indian National Academy of Engineering, Indian National Science Academy, International Fuzzy Systems Association (IFSA), The World Academy of Sciences, and a Fellow of the IEEE, USA.  (

                             💯                                             &nbsꦜp;                                                                       [Editor: Jian Wang]