An Adaptive Transcursive Algorithm for Depth Estimation in Deep Learning Networks

Uthra Kunathur Thikshaja, Anand Paul, Seungmin Rho, Deblina Bhattacharjee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. In this article, we propose a way of using the transformation of functions combined with recursive nature to have an adaptive, transcursive algorithm to represent the backpropagation concept used in deep learning for a Multilayer Perceptron Network. Each function can be used to represent a hidden layer used in the neural network and they can be made to handle a complex part of the processing. Whenever an undesirable output occurs, we transform (modify) the functions until a desirable output is obtained. We have an algorithm that uses the transcursive model to create an interpretation of the concept of deep learning using multilayer perceptron network (MPN).

Original languageEnglish
Title of host publication2016 International Conference on Platform Technology and Service, PlatCon 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467386852
DOIs
StatePublished - 19 Apr 2016
Event3rd International Conference on Platform Technology and Service, PlatCon 2016 - Jeju, Korea, Republic of
Duration: 15 Feb 201617 Feb 2016

Publication series

Name2016 International Conference on Platform Technology and Service, PlatCon 2016 - Proceedings

Conference

Conference3rd International Conference on Platform Technology and Service, PlatCon 2016
Country/TerritoryKorea, Republic of
CityJeju
Period15/02/1617/02/16

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