Learning to sense from events via semantic variational autoencoder (2021)
- Authors:
- USP affiliated authors: MARCACINI, RICARDO MARCONDES - ICMC ; GÔLO, MARCOS PAULO SILVA - ICMC
- Unidade: ICMC
- DOI: 10.1371/journal.pone.0260701
- Subjects: RECONHECIMENTO DE TEXTO; APRENDIZADO COMPUTACIONAL; PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS)
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: San Francisco
- Date published: 2021
- Source:
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
GÔLO, Marcos Paulo Silva e ROSSI, Rafael Geraldeli e MARCACINI, Ricardo Marcondes. Learning to sense from events via semantic variational autoencoder. PLoS ONE, v. 16, n. 2, p. 1-20, 2021Tradução . . Disponível em: https://doi.org/10.1371/journal.pone.0260701. Acesso em: 27 dez. 2025. -
APA
Gôlo, M. P. S., Rossi, R. G., & Marcacini, R. M. (2021). Learning to sense from events via semantic variational autoencoder. PLoS ONE, 16( 2), 1-20. doi:10.1371/journal.pone.0260701 -
NLM
Gôlo MPS, Rossi RG, Marcacini RM. Learning to sense from events via semantic variational autoencoder [Internet]. PLoS ONE. 2021 ; 16( 2): 1-20.[citado 2025 dez. 27 ] Available from: https://doi.org/10.1371/journal.pone.0260701 -
Vancouver
Gôlo MPS, Rossi RG, Marcacini RM. Learning to sense from events via semantic variational autoencoder [Internet]. PLoS ONE. 2021 ; 16( 2): 1-20.[citado 2025 dez. 27 ] Available from: https://doi.org/10.1371/journal.pone.0260701 - One-class edge classification through heterogeneous hypergraph for causal discovery
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Informações sobre o DOI: 10.1371/journal.pone.0260701 (Fonte: oaDOI API)
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