SCIDAR - A Digital Archive of the University of Kragujevac
A digital archive of the University of Kragujevac contains easily searchable and up-to-date research results.
Please use this identifier to cite or link to this item:
https://scidar.kg.ac.rs/handle/123456789/21166| Title: | Effect of the Slope of Symmetric Saturated Activation Functions on Deep Learning |
| Authors: | Lutovac Banduka, Maja  Poucki, Vladimir Mladenovic, Vladimir    Lutovac, Miroslav |
| Journal: | 10th International Scientific Conference Technics, Informatics and Education - TIE 2024 |
| Issue Date: | 2024 |
| Abstract: | It is presented how the slope of symmetric activation functions with saturation affects class detection using symbolic analysis. Different activation functions can be used to increase the most likely detected classes. The main result is the determination of the highest slope of the activation function and the lowest slope of the activation function in terms of the number of neurons in the layer. |
| URI: | https://scidar.kg.ac.rs/handle/123456789/21166 |
| Type: | conferenceObject |
| DOI: | 10.46793/TIE24.079LB |
| Appears in Collections: | Faculty of Technical Sciences, Čačak |
 | Page views(s)189 | Downloads(s)10 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 12 - I.10..pdf | 428.6 kB | Adobe PDF |  View/Open |
This item is licensed under a Creative Commons License
