Research Paper Implementation : Autoencoders, Unsupervised Learning, and Deep Architectures.

ABSTRACT

Autoencoders play a fundamental role in unsupervised learning and in deep architectures for transfer learning and other tasks. In spite of their fundamental role, only linear autoencoders over the real numbers have been solved analytically. Here we present a general mathematical framework for the study of both linear and non-linear autoencoders. The framework allows one to derive an analytical treatment for the most non-linear autoencoder, the Boolean autoencoder. Learning in the Boolean autoencoder is equivalent to a clustering problem that can be solved in polynomial time when the number of clusters is small and becomes NP complete when the number of clusters is large. The framework sheds light on the different kinds of autoencoders, their learning complexity, their horizontal and vertical composability in deep architectures, their critical points, and their fundamental connections to clustering, Hebbian learning, and information theory.


KEYWORDS

autoencoders, unsupervised learning, compression, clustering, principal component analysis, boolean, complexity, deep architectures, hebbian learning, information theory.



To download full research paper click on the link below.

AUTOENCODER-CODERSARTS
.pdf
Download PDF • 354KB




If you need implementation of this research paper or any of its variants, feel free contact us on contact@codersarts.com.

Contact Us

Tel: (+91) 0120  4118730  

Time :   10 : 00  AM -  08 : 00 PM IST 

Registered address: G-69, Sector 63, 

 Noida - 201301, India

We Provide Services Across The different countries

USA    Australia   Canada   UK    UAE    Singapore   New Zealand    Malasia   India   Ireland   Germany

CodersArts is a Product by Sofstack Technology Solutions Pvt. Ltd.

  • CodersArts | Linkedin
  • Instagram