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Addressing the gap between current language models and key-term-based clustering (2023)
Cabral, Eric Macedo ; Rezaeipourfarsangi, Sima ; Oliveira, Maria Cristina Ferreira de ; Milios, Evangelos E ; Minghim, Rosane
Source: Proceedings. Conference titles: ACM Symposium on Document Engineering - DocEng. Unidade: ICMC
Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DE TEXTO, REDES NEURAIS, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CABRAL, Eric Macedo et al. Addressing the gap between current language models and key-term-based clustering. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3573128.3604900. Acesso em: 01 jul. 2024.
APA
Cabral, E. M., Rezaeipourfarsangi, S., Oliveira, M. C. F. de, Milios, E. E., & Minghim, R. (2023). Addressing the gap between current language models and key-term-based clustering. In Proceedings. New York: ACM. doi:10.1145/3573128.3604900
NLM
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2024 jul. 01 ] Available from: https://doi.org/10.1145/3573128.3604900
Vancouver
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2024 jul. 01 ] Available from: https://doi.org/10.1145/3573128.3604900
Artigo de periodico
Neural network training fingerprint: visual analytics of the training process in classification neural networks (2022)
Ferreira, Martha Dais ; Cantareira, Gabriel Dias ; Mello, Rodrigo Fernandes de ; Paulovich, Fernando Vieira
Source: Journal of Visualization. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERREIRA, Martha Dais et al. Neural network training fingerprint: visual analytics of the training process in classification neural networks. Journal of Visualization, v. 25, n. 3, p. 593-612, 2022Tradução . . Disponível em: https://doi.org/10.1007/s12650-021-00809-4. Acesso em: 01 jul. 2024.
APA
Ferreira, M. D., Cantareira, G. D., Mello, R. F. de, & Paulovich, F. V. (2022). Neural network training fingerprint: visual analytics of the training process in classification neural networks. Journal of Visualization, 25( 3), 593-612. doi:10.1007/s12650-021-00809-4
NLM
Ferreira MD, Cantareira GD, Mello RF de, Paulovich FV. Neural network training fingerprint: visual analytics of the training process in classification neural networks [Internet]. Journal of Visualization. 2022 ; 25( 3): 593-612.[citado 2024 jul. 01 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Vancouver
Ferreira MD, Cantareira GD, Mello RF de, Paulovich FV. Neural network training fingerprint: visual analytics of the training process in classification neural networks [Internet]. Journal of Visualization. 2022 ; 25( 3): 593-612.[citado 2024 jul. 01 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Artigo de periodico
A graph-based approach for positive and unlabeled learning (2021)
Carnevali, Julio César ; Rossi, Rafael Geraldeli ; Milios, Evangelos ; Lopes, Alneu de Andrade
Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE TEXTO, RECUPERAÇÃO DA INFORMAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARNEVALI, Julio César et al. A graph-based approach for positive and unlabeled learning. Information Sciences, v. No 2021, p. 655-672, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2021.08.099. Acesso em: 01 jul. 2024.
APA
Carnevali, J. C., Rossi, R. G., Milios, E., & Lopes, A. de A. (2021). A graph-based approach for positive and unlabeled learning. Information Sciences, No 2021, 655-672. doi:10.1016/j.ins.2021.08.099
NLM
Carnevali JC, Rossi RG, Milios E, Lopes A de A. A graph-based approach for positive and unlabeled learning [Internet]. Information Sciences. 2021 ; No 2021 655-672.[citado 2024 jul. 01 ] Available from: https://doi.org/10.1016/j.ins.2021.08.099
Vancouver
Carnevali JC, Rossi RG, Milios E, Lopes A de A. A graph-based approach for positive and unlabeled learning [Internet]. Information Sciences. 2021 ; No 2021 655-672.[citado 2024 jul. 01 ] Available from: https://doi.org/10.1016/j.ins.2021.08.099
Artigo de periodico
Explainable matrix: visualization for global and local interpretability of random forest classification ensembles (2021)
Popolin Neto, Mário ; Paulovich, Fernando Vieira
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POPOLIN NETO, Mário e PAULOVICH, Fernando Vieira. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 1427-1437, 2021Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2020.3030354. Acesso em: 01 jul. 2024.
APA
Popolin Neto, M., & Paulovich, F. V. (2021). Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, 27( 2), 1427-1437. doi:10.1109/TVCG.2020.3030354
NLM
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2024 jul. 01 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354
Vancouver
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2024 jul. 01 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354
Artigo de periodico
Random forest similarity maps: a scalable visual representation for global and local interpretation (2021)
Mazumdar, Dipankar ; Popolin Neto, Mário ; Paulovich, Fernando Vieira
Source: Electronics. Unidade: ICMC
Subjects: VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAZUMDAR, Dipankar e POPOLIN NETO, Mário e PAULOVICH, Fernando Vieira. Random forest similarity maps: a scalable visual representation for global and local interpretation. Electronics, v. 10, p. 1-20, 2021Tradução . . Disponível em: https://doi.org/10.3390/electronics10222862. Acesso em: 01 jul. 2024.
APA
Mazumdar, D., Popolin Neto, M., & Paulovich, F. V. (2021). Random forest similarity maps: a scalable visual representation for global and local interpretation. Electronics, 10, 1-20. doi:10.3390/electronics10222862
NLM
Mazumdar D, Popolin Neto M, Paulovich FV. Random forest similarity maps: a scalable visual representation for global and local interpretation [Internet]. Electronics. 2021 ; 10 1-20.[citado 2024 jul. 01 ] Available from: https://doi.org/10.3390/electronics10222862
Vancouver
Mazumdar D, Popolin Neto M, Paulovich FV. Random forest similarity maps: a scalable visual representation for global and local interpretation [Internet]. Electronics. 2021 ; 10 1-20.[citado 2024 jul. 01 ] Available from: https://doi.org/10.3390/electronics10222862

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