Scalable Activation Function Employment in Higher Order Neural Networks in Tasks of Supervised Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00366751" target="_blank" >RIV/68407700:21220/23:00366751 - isvavai.cz</a>
Result on the web
<a href="http://ysesm2023.itam.cas.cz/index.php" target="_blank" >http://ysesm2023.itam.cas.cz/index.php</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Scalable Activation Function Employment in Higher Order Neural Networks in Tasks of Supervised Learning
Original language description
Present contribution deals with methods of supervised learning. A novel approach - scalable activation function and its influence to speed of convergence is studied. Furthermore, unlike classical deep learning approaches, presented neural networks architectures are built with higher order neural units that enable to capture higher levels of non-linearity in tasks. Basics of higher order neural networks and learnable activation function are introduced. The applicability of aforementioned approach is tested and proved on various well-known task of supervised learning, e.g., unknown non-linear functions introduced by Klassen, Gupta et al. and well-known logic function - XOR problem. It turned out that presented method outperformed standard deep learning methods in terms of convergence speed.
Czech name
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Czech description
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Project
<a href="/en/project/EF16_019%2F0000826" target="_blank" >EF16_019/0000826: Center of Advanced Aerospace Technology</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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