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Dissertação (Mestrado)
Neural networks for out-of-distribution time series detection in one-class learning (2025)
Medeiros Júnior, José Gilberto Barbosa de ; Silva, Diego Furtado (Orientador)
Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MEDEIROS JÚNIOR, José Gilberto Barbosa de. Neural networks for out-of-distribution time series detection in one-class learning. 2025. Dissertação (Mestrado) – Universidade de São Paulo, São Carlos, 2025. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-29072025-141957/. Acesso em: 10 fev. 2026.
APA
Medeiros Júnior, J. G. B. de. (2025). Neural networks for out-of-distribution time series detection in one-class learning (Dissertação (Mestrado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-29072025-141957/
NLM
Medeiros Júnior JGB de. Neural networks for out-of-distribution time series detection in one-class learning [Internet]. 2025 ;[citado 2026 fev. 10 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-29072025-141957/
Vancouver
Medeiros Júnior JGB de. Neural networks for out-of-distribution time series detection in one-class learning [Internet]. 2025 ;[citado 2026 fev. 10 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-29072025-141957/
Artigo de periodico
One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication (2025)
Gôlo, Marcos Paulo Silva ; Medeiros Júnior, José Gilberto Barbosa de ; Silva, Diego Furtado ; Marcacini, Ricardo Marcondes
Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, TEORIA DOS GRAFOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GÔLO, Marcos Paulo Silva et al. One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication. Information Sciences, v. 708, p. 1-17, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2025.122060. Acesso em: 10 fev. 2026.
APA
Gôlo, M. P. S., Medeiros Júnior, J. G. B. de, Silva, D. F., & Marcacini, R. M. (2025). One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication. Information Sciences, 708, 1-17. doi:10.1016/j.ins.2025.122060
NLM
Gôlo MPS, Medeiros Júnior JGB de, Silva DF, Marcacini RM. One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication [Internet]. Information Sciences. 2025 ; 708 1-17.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1016/j.ins.2025.122060
Vancouver
Gôlo MPS, Medeiros Júnior JGB de, Silva DF, Marcacini RM. One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication [Internet]. Information Sciences. 2025 ; 708 1-17.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1016/j.ins.2025.122060
Trabalho de evento-anais periodico
A heterogeneous network-based positive and unlabeled learning approach to detect fake news (2021)
Souza, Mariana Caravanti de ; Nogueira, Bruno Magalhães ; Rossi, Rafael Geraldeli ; Marcacini, Ricardo Marcondes ; Rezende, Solange Oliveira
Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Subjects: FAKE NEWS, PROCESSAMENTO DE TEXTO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Mariana Caravanti de et al. A heterogeneous network-based positive and unlabeled learning approach to detect fake news. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91699-2_1. Acesso em: 10 fev. 2026. , 2021
APA
Souza, M. C. de, Nogueira, B. M., Rossi, R. G., Marcacini, R. M., & Rezende, S. O. (2021). A heterogeneous network-based positive and unlabeled learning approach to detect fake news. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-91699-2_1
NLM
Souza MC de, Nogueira BM, Rossi RG, Marcacini RM, Rezende SO. A heterogeneous network-based positive and unlabeled learning approach to detect fake news [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13074 3-18.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_1
Vancouver
Souza MC de, Nogueira BM, Rossi RG, Marcacini RM, Rezende SO. A heterogeneous network-based positive and unlabeled learning approach to detect fake news [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13074 3-18.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_1

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