典型文献
On the principles of Parsimony and Self-consistency for the emergence of intelligence
文献摘要：
Ten years into the revival of deep networks and artificial intelligence,we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of intelligence in general.We introduce two fundamental principles,Parsimony and Self-consistency,which address two fundamental questions regarding intelligence:what to learn and how to learn,respectively.We believe the two principles serve as the cornerstone for the emergence of intelligence,artificial or natural.While they have rich classical roots,we argue that they can be stated anew in entirely measurable and computable ways.More specifically,the two principles lead to an effective and efficient computational framework,compressive closed-loop transcription,which unifies and explains the evolution of modern deep networks and most practices of artificial intelligence.While we use mainly visual data modeling as an example,we believe the two principles will unify understanding of broad families of autonomous intelligent systems and provide a framework for understanding the brain.
文献关键词：
中图分类号：
作者姓名：
Yi MA;Doris TSAO;Heung-Yeung SHUM
作者机构：
Electrical Engineering and Computer Science Department,University of California,Berkeley,CA 94720,USA;Department of Molecular&Cell Biology and Howard Hughes Medical Institute,University of California,Berkeley,CA 94720,USA;International Digital Economy Academy,Shenzhen 518045,China
文献出处：
引用格式：
[1]Yi MA;Doris TSAO;Heung-Yeung SHUM-.On the principles of Parsimony and Self-consistency for the emergence of intelligence)[J].信息与电子工程前沿（英文）,2022(09):1298-1323
A类：
Parsimony,anew
B类：
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AB值：
0.587553
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