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Multi-kernel Covariance Terms in Multi-output Support Vector Machines Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Unsupervisedly Learned Representations – Should the Quest Be Over?

Author : Daniel N. Nissani (Nissensohn)

Published in: Machine Learning, Optimization, and Data Science

Publisher: Springer International Publishing

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Abstract

After four decades of research there still exists a Classification accuracy gap of about 20% between our best Unsupervisedly Learned Representations methods and the accuracy rates achieved by intelligent animals. It thus may well be that we are looking in the wrong direction. A possible solution to this puzzle is presented. We demonstrate that Reinforcement Learning can learn representations which achieve the same accuracy as that of animals. Our main modest contribution lies in the observations that: a. when applied to a real world environment Reinforcement Learning does not require labels, and thus may be legitimately considered as Unsupervised Learning, and b. in contrast, when Reinforcement Learning is applied in a simulated environment it does inherently require labels and should thus be generally be considered as Supervised Learning. The corollary of these observations is that further search for Unsupervised Learning competitive paradigms which may be trained in simulated environments may be futile.

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Footnotes
1
See e.g. Yann LeCun Interview, IEEE Spectrum, Feb. 2015: “…The bottom line is that the brain is much better than our model at doing unsupervised learning…”.
 
2
So that, like Moses on Mount Nebo, we may be able to see Promised Land but will never reach it (Deuteronomy 34:4).
 
4
Matlab code of this simple setup will be available upon request from the Author.
 
5
We could consider in principle the case wherein the Environment predicts by itself a label from the Dog’s photograph; this however requires the Environment to be a trained classifier, which enters us into an endless loop.
 
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Metadata
Title
Unsupervisedly Learned Representations – Should the Quest Be Over?
Author
Daniel N. Nissani (Nissensohn)
Copyright Year
2020
Book
Machine Learning, Optimization, and Data Science

Print ISBN: 978-3-030-64579-3

Electronic ISBN: 978-3-030-64580-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-64580-9_29

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