Artificial neural network weights penalization and initialization for spectroscopic data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APU142523" target="_blank" >RIV/00216305:26620/21:PU142523 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Artificial neural network weights penalization and initialization for spectroscopic data

Popis výsledku v původním jazyce

Nowadays, Artificial Neural Networks (ANNs) are among the most utilized techniques for the advanced processing of spectroscopic data. However, several problems have emerged from such a broad adoption that are limiting their performance and trustworthiness. The most significant shortcomings are: 1) “blackbox-like” utilization of ANNs, 2) overtrained and overparametrized models, and 3) a direct transition of ANN architecture from different tasks and data types (e.g. image processing). In this work, we mainly focus on the third mentioned problem and propose several adjustments to the architecture and learning process of ANNs, which are suitable for spectroscopic data. Our approach is based on the unique properties of spectroscopic data and their direct exploitation in form of special weight initialization strategies or penalizations of the loss function. Adjusted models provide improved analytical performance and interpretability.

Název v anglickém jazyce

Artificial neural network weights penalization and initialization for spectroscopic data

Popis výsledku anglicky

Nowadays, Artificial Neural Networks (ANNs) are among the most utilized techniques for the advanced processing of spectroscopic data. However, several problems have emerged from such a broad adoption that are limiting their performance and trustworthiness. The most significant shortcomings are: 1) “blackbox-like” utilization of ANNs, 2) overtrained and overparametrized models, and 3) a direct transition of ANN architecture from different tasks and data types (e.g. image processing). In this work, we mainly focus on the third mentioned problem and propose several adjustments to the architecture and learning process of ANNs, which are suitable for spectroscopic data. Our approach is based on the unique properties of spectroscopic data and their direct exploitation in form of special weight initialization strategies or penalizations of the loss function. Adjusted models provide improved analytical performance and interpretability.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Kvalitní interní granty VUT</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů