Exploring the impact of post-training rounding in regression models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00585135" target="_blank" >RIV/67985807:_____/24:00585135 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.21136/AM.2024.0090-23" target="_blank" >https://doi.org/10.21136/AM.2024.0090-23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21136/AM.2024.0090-23" target="_blank" >10.21136/AM.2024.0090-23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Exploring the impact of post-training rounding in regression models

Popis výsledku v původním jazyce

Post-training rounding, also known as quantization, of estimated parameters stands as a widely adopted technique for mitigating energy consumption and latency in machine learning models. This theoretical endeavor delves into the examination of the impact of rounding estimated parameters in key regression methods within the realms of statistics and machine learning. The proposed approach allows for the perturbation of parameters through an additive error with values within a specified interval. This method is elucidated through its application to linear regression and is subsequently extended to encompass radial basis function networks, multilayer perceptrons, regularization networks, and logistic regression, maintaining a consistent approach throughout.

Název v anglickém jazyce

Exploring the impact of post-training rounding in regression models

Popis výsledku anglicky

Post-training rounding, also known as quantization, of estimated parameters stands as a widely adopted technique for mitigating energy consumption and latency in machine learning models. This theoretical endeavor delves into the examination of the impact of rounding estimated parameters in key regression methods within the realms of statistics and machine learning. The proposed approach allows for the perturbation of parameters through an additive error with values within a specified interval. This method is elucidated through its application to linear regression and is subsequently extended to encompass radial basis function networks, multilayer perceptrons, regularization networks, and logistic regression, maintaining a consistent approach throughout.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Aproximativní neurovýpočty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Applications of Mathematics

ISSN

0862-7940

e-ISSN

1572-9109

Svazek periodika

69

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

15

Strana od-do

257-271

Kód UT WoS článku

001181978000001

EID výsledku v databázi Scopus

2-s2.0-85187671548