Item Type |
Journal paper
|
Abstract |
There is some theoretical evidence that deep neural networks with multiple hidden layers have a potential for more efficient representation of multidimensional mappings than shallow networks with a single hidden layer. The question is whether it is possible to exploit this theoretical advantage for finding such representations with help of numerical training methods. Tests using prototypical problems with a known mean square minimum did not confirm this hypothesis. Minima found with the help of deep networks have always been worse than those found using shallow networks. This does not directly contradict the theoretical findings—it is possible that the superior representational capacity of deep networks is genuine while finding the mean square minimum of such deep networks is a substantially harder problem than with shallow ones. |
Authors |
Bermeitinger, Bernhard; Hrycej, Tomas & Handschuh, Siegfried |
Journal or Publication Title |
Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management |
Language |
English |
Subjects |
computer science |
HSG Classification |
contribution to scientific community |
HSG Profile Area |
None |
Refereed |
Yes |
Date |
September 2019 |
Publisher |
SCITEPRESS - Science and Technology Publications |
Page Range |
532-538 |
ISSN |
978-989-758-382-7 |
Publisher DOI |
https://doi.org/10.5220/0008364305320538 |
Official URL |
http://www.scitepress.org/DigitalLibrary/Link.aspx... |
Contact Email Address |
bernhard.bermeitinger@unisg.ch |
Depositing User |
Bernhard Bermeitinger
|
Date Deposited |
02 Oct 2019 10:01 |
Last Modified |
20 Jul 2022 17:39 |
URI: |
https://www.alexandria.unisg.ch/publications/257996 |