EVOLVE·INFOMAX: An Unsupervised Learning Principle of Invariances for Nonlinear Dynamic Systems
Author:
Affiliation:
1. Department of Automation Engineering, Technical University of Ilmenau, Ilmenau, Thuringia98684, Germany
Publisher
American Chemical Society (ACS)
Subject
Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c03330
Reference38 articles.
1. CONNECTIONIST LEARNING PROCEDURES11This chapter appeared in Volume 40 of Artificial Intelligence in 1989, reprinted with permission of North-Holland Publishing. It is a revised version of Technical Report CMU-CS-87-115, which has the same title and was prepared in June 1987 while the author was at Carnegie Mellon University. The research was supported by contract N00014-86-K-00167 from the Office of Naval Research and by grant IST-8520359 from the National Science Foundation.
2. Learning Invariance from Transformation Sequences
3. Removing Time Variation with the Anti-Hebbian Differential Synapse
4. Self-organizing neural network that discovers surfaces in random-dot stereograms
5. Slow Feature Analysis: Unsupervised Learning of Invariances
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