I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Learning Textual Representations from Multiple Modalities t..:
, In:
Proceedings of the Brazilian Symposium on Multimedia and the Web
,
Gôlo, Marcos
;
Caravanti, Mariana
;
Rossi, Rafael
... - p. 197-204 , 2021
Link:
https://dl.acm.org/doi/10.1145/3470482.3479634
RT T1 <cTitle>Proceedings of the Brazilian Symposium on Multimedia and the Web</cTitle>: T1 <Title>Learning Textual Representations from Multiple Modalities to Detect Fake News Through One-Class Learning</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3479634&Exemplar=1&LAN=DE A1 Gôlo, Marcos A1 Caravanti, Mariana A1 Rossi, Rafael A1 Rezende, Solange A1 Nogueira, Bruno A1 Marcacini, Ricardo PB ACM YR 2021 K1 Fake News Classification K1 Multimodal Variational Autoencoder K1 One-Class Classification K1 One-Class Fake News Classification K1 Computing methodologies K1 Artificial intelligence K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 197 OP 204 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3470482.3479634 DO https://dl.acm.org/doi/10.1145/3470482.3479634 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
