A Theory Explains Deep Learning
| Authors |
|
| Abstract |
This is our journal for developing Deduction Theory and studying Deep Learning and Artificial intelligence. Deduction Theory is a Theory of Deducing World’s Relativity by Information Coupling and Asymmetry. We focus on information processing, see intelligence as an information structure that relatively close object-oriented, probability-oriented, unsupervised learning, relativity information processing and massive automated information processing. We see deep learning and machine learning as an attempt to make all types of information processing relatively close to probability information processing. We will discuss about how to understand Deep Learning and Artificial intelligence and why Deep Learning is shown better performance than the other methods by metaphysical logic.
|
| Keywords | Information theory Computational logic Metaphysics |
| Categories | (categorize this paper) |
| Options |
Edit this record
Mark as duplicate
Export citation
Request removal from index
|
Download options
External links
(no proxy) Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library
- Only published works are available at libraries.
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Quantum Deep Learning Triuniverse.Angus McCoss - 2016 - Journal of Quantum Information Science 6 (4).
The Synergy of Learning Organisations and Flexible Information Technology.George Rzevski & Kumkum Prasad - 1998 - AI and Society 12 (1-2):87-96.
Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing.Axel Cleeremans - 1993 - MIT Press.
Controlled and Automatic Human Information Processing: Perceptual Learning, Automatic Attending, and a General Theory.Richard M. Shiffrin & Walter E. Schneider - 1977 - Psychological Review 84 (2):128-90.
Information Theory and Immediate Recall.Murray Aborn & Herbert Rubenstein - 1952 - Journal of Experimental Psychology 44 (4):260.
Emergence of Information Transfer by Inductive Learning.Simon M. Huttegger & Brian Skyrms - 2008 - Studia Logica 89 (2):237-256.
Information-Processing Analysis of Verbal Learning.William A. Johnston, Rollie R. Wagstaff & Douglas Griffith - 1972 - Journal of Experimental Psychology 96 (2):307.
Notationality and the Information Processing Mind.Vinod Goel - 1991 - Minds and Machines 1 (2):129-166.
Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar. StuartmShieber - unknown
Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.Shalom Lappin - unknown
Information Processing, Computation, and Cognition.Gualtiero Piccinini & Andrea Scarantino - 2011 - Journal of Biological Physics 37 (1):1-38.
Analysis on Mental Structures in Language Learning.Ya-Ping Cui - 2005 - Philosophy of the Social Sciences 35 (3):147-150.
Philosophy and Machine Learning.Paul R. Thagard - 1990 - Canadian Journal of Philosophy 20 (2):261-76.
Analytics
Added to PP index
2017-01-09
Total downloads
261 ( #18,048 of 2,293,866 )
Recent downloads (6 months)
36 ( #11,279 of 2,293,866 )
2017-01-09
Total downloads
261 ( #18,048 of 2,293,866 )
Recent downloads (6 months)
36 ( #11,279 of 2,293,866 )
How can I increase my downloads?
Monthly downloads
