Fine-grained position helps memorizing more, a novel music compound transformer model with feature interaction fusion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AI2BNJU9U" target="_blank" >RIV/00216208:11320/23:I2BNJU9U - isvavai.cz</a>

Result on the web

<a href="https://ojs.aaai.org/index.php/AAAI/article/view/25650" target="_blank" >https://ojs.aaai.org/index.php/AAAI/article/view/25650</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/aaai.v37i4.25650" target="_blank" >10.1609/aaai.v37i4.25650</a>

Result language

angličtina

Original language name

Fine-grained position helps memorizing more, a novel music compound transformer model with feature interaction fusion

Original language description

"Due to the particularity of the simultaneous occurrence of multiple events in music sequences, compound Transformer is proposed to deal with the challenge of long sequences. However, there are two deficiencies in the compound Transformer."

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

—

Continuities

—

Publication year

2023

Confidentiality

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

Article name in the collection

"Proceedings of the AAAI Conference on Artificial Intelligence"

ISBN

978-1-57735-880-0

ISSN

2159-5399

e-ISSN

—

Number of pages

10

Pages from-to

5203-5212

Publisher name

arXiv

Place of publication

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Event location

Toronto, Canada

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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