Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00351376" target="_blank" >RIV/68407700:21240/21:00351376 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-86340-1_19" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-86340-1_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-86340-1_19" target="_blank" >10.1007/978-3-030-86340-1_19</a>

Result language

angličtina

Original language name

Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks

Original language description

Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks, where now excessively large models are used. However, such models face several problems during the learning process, mainly due to the redundancy of the individual neurons, which results in sub-optimal accuracy or the need for additional training steps. Here, we explore the diversity of the neurons within the hidden layer during the learning process, and analyze how the diversity of the neurons affects predictions of the model. As following, we introduce several techniques to dynamically reinforce diversity between neurons during the training. These decorrelation techniques improve learning at early stages and occasionally help to overcome local minima faster. Additionally, we describe novel weight initialization method to obtain decorrelated, yet stochastic weight initialization for a fast and efficient neural network training. Decorrelated weight initialization in our case shows about 40% relative increase in test accuracy during the first 5 epochs.

Czech name

—

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

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

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 2021

ISBN

978-3-030-86339-5

ISSN

—

e-ISSN

1611-3349

Number of pages

13

Pages from-to

235-247

Publisher name

Springer

Place of publication

Cham

Event location

Bratislava

Event date

Sep 14, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000711922300019