On the Bayesian Interpretation of Robust Regression Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00600652" target="_blank" >RIV/67985807:_____/24:00600652 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-72332-2_3" target="_blank" >https://doi.org/10.1007/978-3-031-72332-2_3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-72332-2_3" target="_blank" >10.1007/978-3-031-72332-2_3</a>

Result language

angličtina

Original language name

On the Bayesian Interpretation of Robust Regression Neural Networks

Original language description

The aim of this work is to search for intuitive interpretations of regularized regression procedures within the framework of Bayesian inference. First, the paper considers Bayesian estimation of parameters of the linear regression model. Second, regularized neural networks are explained to correspond to the Bayesian approach obtained under specific assumptions. The contribution is a unique compact look on training neural networks with available prior information, i.e. a likelihood-based perspective of training neural networks. Attention is also paid to very recently proposed regularized versions of robust neural networks, as a novelty, these are expressed by means of quasi-likelihood and their connection to Bayesian reasoning is discussed as well.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

Artificial Neural Networks and Machine Learning – ICANN 2024. Proceedings Part I

ISBN

978-3-031-72331-5

ISSN

—

e-ISSN

—

Number of pages

11

Pages from-to

30-40

Publisher name

Springer

Place of publication

Cham

Event location

Lugano

Event date

Sep 17, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001331868600003