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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
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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: 22 jun. 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 jun. 22 ] 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 jun. 22 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Artigo de periodico
Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination (2022)
Aradhya, Abhay M. S ; Ashfahani, Andri; Angelina, Fienny; Pratama, Mahardhika ; Mello, Rodrigo Fernandes de ; Sundaram, Suresh
Source: Information Sciences. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, COMPUTAÇÃO EVOLUTIVA
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ABNT
ARADHYA, Abhay M. S et al. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination. Information Sciences, v. 607, p. 638-653, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2022.05.100. Acesso em: 22 jun. 2024.
APA
Aradhya, A. M. S., Ashfahani, A., Angelina, F., Pratama, M., Mello, R. F. de, & Sundaram, S. (2022). Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination. Information Sciences, 607, 638-653. doi:10.1016/j.ins.2022.05.100
NLM
Aradhya AMS, Ashfahani A, Angelina F, Pratama M, Mello RF de, Sundaram S. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination [Internet]. Information Sciences. 2022 ; 607 638-653.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.ins.2022.05.100
Vancouver
Aradhya AMS, Ashfahani A, Angelina F, Pratama M, Mello RF de, Sundaram S. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination [Internet]. Information Sciences. 2022 ; 607 638-653.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.ins.2022.05.100
Trabalho de evento
Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos (2021)
Nazaré, Tiago Santana de ; Mello, Rodrigo Fernandes de ; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, RECONHECIMENTO DE IMAGEM, VÍDEO
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ABNT
NAZARÉ, Tiago Santana de e MELLO, Rodrigo Fernandes de e PONTI, Moacir Antonelli. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos. 2021, Anais.. Lisboa: SciTePress, 2021. Disponível em: https://doi.org/10.5220/0010347907530762. Acesso em: 22 jun. 2024.
APA
Nazaré, T. S. de, Mello, R. F. de, & Ponti, M. A. (2021). Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos. In Proceedings. Lisboa: SciTePress. doi:10.5220/0010347907530762
NLM
Nazaré TS de, Mello RF de, Ponti MA. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos [Internet]. Proceedings. 2021 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.5220/0010347907530762
Vancouver
Nazaré TS de, Mello RF de, Ponti MA. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos [Internet]. Proceedings. 2021 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.5220/0010347907530762
Artigo de periodico
Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification (2020)
Canário, João Paulo ; Mello, Rodrigo Fernandes de ; Curilem, Millaray ; Huenupan, Fernando ; Rios, Ricardo Araújo
Source: Data in Brief. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS, VULCÕES
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ABNT
CANÁRIO, João Paulo et al. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, v. 30, p. 1-6, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.dib.2020.105627. Acesso em: 22 jun. 2024.
APA
Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, 30, 1-6. doi:10.1016/j.dib.2020.105627
NLM
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.dib.2020.105627
Vancouver
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.dib.2020.105627
Trabalho de evento-resumo
Random projections and α-shape to support the kernel design (2020)
Cestari, Daniel Moreira ; Mello, Rodrigo Fernandes de
Source: Proceedings. Conference titles: AAAI Conference on Artificial Intelligence - AAAI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, RECONHECIMENTO DE PADRÕES
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ABNT
CESTARI, Daniel Moreira e MELLO, Rodrigo Fernandes de. Random projections and α-shape to support the kernel design. 2020, Anais.. Palo Alto: AAAI Press, 2020. Disponível em: https://doi.org/10.1609/aaai.v34i10.7211. Acesso em: 22 jun. 2024.
APA
Cestari, D. M., & Mello, R. F. de. (2020). Random projections and α-shape to support the kernel design. In Proceedings. Palo Alto: AAAI Press. doi:10.1609/aaai.v34i10.7211
NLM
Cestari DM, Mello RF de. Random projections and α-shape to support the kernel design [Internet]. Proceedings. 2020 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1609/aaai.v34i10.7211
Vancouver
Cestari DM, Mello RF de. Random projections and α-shape to support the kernel design [Internet]. Proceedings. 2020 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1609/aaai.v34i10.7211
Artigo de periodico
In-depth comparison of deep artificial neural network architectures on seismic events classification (2020)
Canário, João Paulo ; Mello, Rodrigo Fernandes de ; Curilem, Millaray ; Huenupan, Fernando ; Rios, Ricardo Araújo
Source: Journal of Volcanology and Geothermal Research. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS, VULCÕES, ANÁLISE DE ONDALETAS
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ABNT
CANÁRIO, João Paulo et al. In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, v. 401, p. Se 2020, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.jvolgeores.2020.106881. Acesso em: 22 jun. 2024.
APA
Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, 401, Se 2020. doi:10.1016/j.jvolgeores.2020.106881
NLM
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
Vancouver
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
Artigo de periodico
Theoretical learning guarantees applied to acoustic modeling (2019)
Shulby, Christopher D; Ferreira, Martha D; Mello, Rodrigo Fernandes de; Aluísio, Sandra Maria
Source: Journal of the Brazilian Computer Society. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE VOZ
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ABNT
SHULBY, Christopher D et al. Theoretical learning guarantees applied to acoustic modeling. Journal of the Brazilian Computer Society, v. 25, p. 1-12, 2019Tradução . . Disponível em: https://doi.org/10.1186/s13173-018-0081-3. Acesso em: 22 jun. 2024.
APA
Shulby, C. D., Ferreira, M. D., Mello, R. F. de, & Aluísio, S. M. (2019). Theoretical learning guarantees applied to acoustic modeling. Journal of the Brazilian Computer Society, 25, 1-12. doi:10.1186/s13173-018-0081-3
NLM
Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Theoretical learning guarantees applied to acoustic modeling [Internet]. Journal of the Brazilian Computer Society. 2019 ; 25 1-12.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1186/s13173-018-0081-3
Vancouver
Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Theoretical learning guarantees applied to acoustic modeling [Internet]. Journal of the Brazilian Computer Society. 2019 ; 25 1-12.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1186/s13173-018-0081-3
Artigo de periodico
Designing architectures of convolutional neural networks to solve practical problems (2018)
Ferreira, Martha Dais; Corrêa, Débora Cristina; Nonato, Luis Gustavo ; Mello, Rodrigo Fernandes de
Source: Expert Systems with Applications. Unidade: ICMC
Subjects: REDES NEURAIS, SISTEMAS DINÂMICOS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE OBJETOS
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ABNT
FERREIRA, Martha Dais et al. Designing architectures of convolutional neural networks to solve practical problems. Expert Systems with Applications, v. 94, p. 205-217, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2017.10.052. Acesso em: 22 jun. 2024.
APA
Ferreira, M. D., Corrêa, D. C., Nonato, L. G., & Mello, R. F. de. (2018). Designing architectures of convolutional neural networks to solve practical problems. Expert Systems with Applications, 94, 205-217. doi:10.1016/j.eswa.2017.10.052
NLM
Ferreira MD, Corrêa DC, Nonato LG, Mello RF de. Designing architectures of convolutional neural networks to solve practical problems [Internet]. Expert Systems with Applications. 2018 ; 94 205-217.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.eswa.2017.10.052
Vancouver
Ferreira MD, Corrêa DC, Nonato LG, Mello RF de. Designing architectures of convolutional neural networks to solve practical problems [Internet]. Expert Systems with Applications. 2018 ; 94 205-217.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1016/j.eswa.2017.10.052
Trabalho de evento
Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks (2018)
Nazaré, Tiago Santana de ; Costa, Gabriel B. Paranhos da; Mello, Rodrigo Fernandes de; Ponti, Moacir Antonelli
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, VISÃO COMPUTACIONAL
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ABNT
NAZARÉ, Tiago Santana de et al. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. 2018, Anais.. Los Alamitos: IEEE, 2018. Disponível em: https://doi.org/10.1109/SIBGRAPI.2018.00053. Acesso em: 22 jun. 2024.
APA
Nazaré, T. S. de, Costa, G. B. P. da, Mello, R. F. de, & Ponti, M. A. (2018). Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI.2018.00053
NLM
Nazaré TS de, Costa GBP da, Mello RF de, Ponti MA. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks [Internet]. Proceedings. 2018 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00053
Vancouver
Nazaré TS de, Costa GBP da, Mello RF de, Ponti MA. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks [Internet]. Proceedings. 2018 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00053
Trabalho de evento
Acoustic modeling using a shallow CNN-HTSVM architecture (2017)
Shulby, Christopher Dane; Ferreira, Martha Dais; Mello, Rodrigo Fernandes de; Aluísio, Sandra Maria
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE VOZ
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SHULBY, Christopher Dane et al. Acoustic modeling using a shallow CNN-HTSVM architecture. 2017, Anais.. Piscataway: IEEE, 2017. Disponível em: https://doi.org/10.1109/BRACIS.2017.62. Acesso em: 22 jun. 2024.
APA
Shulby, C. D., Ferreira, M. D., Mello, R. F. de, & Aluísio, S. M. (2017). Acoustic modeling using a shallow CNN-HTSVM architecture. In Proceedings. Piscataway: IEEE. doi:10.1109/BRACIS.2017.62
NLM
Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Acoustic modeling using a shallow CNN-HTSVM architecture [Internet]. Proceedings. 2017 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1109/BRACIS.2017.62
Vancouver
Shulby CD, Ferreira MD, Mello RF de, Aluísio SM. Acoustic modeling using a shallow CNN-HTSVM architecture [Internet]. Proceedings. 2017 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1109/BRACIS.2017.62
Trabalho de evento
Data clustering using topological features (2014)
Pereira, Cássio M. M; Mello, Rodrigo Fernandes de
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Subjects: REDES NEURAIS, PROGRAMAÇÃO CONCORRENTE, SISTEMAS DINÂMICOS
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ABNT
PEREIRA, Cássio M. M e MELLO, Rodrigo Fernandes de. Data clustering using topological features. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/BRACIS.2014.71. Acesso em: 22 jun. 2024.
APA
Pereira, C. M. M., & Mello, R. F. de. (2014). Data clustering using topological features. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2014.71
NLM
Pereira CMM, Mello RF de. Data clustering using topological features [Internet]. Proceedings. 2014 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1109/BRACIS.2014.71
Vancouver
Pereira CMM, Mello RF de. Data clustering using topological features [Internet]. Proceedings. 2014 ;[citado 2024 jun. 22 ] Available from: https://doi.org/10.1109/BRACIS.2014.71
Artigo de periodico
Image indexing and retrieval using an ART-2A neural network architecture (2008)
Mello, Rodrigo Fernandes de; Bueno, Josiane Maria; Senger, Luciano José; Yang, Laurence T
Source: International Journal of Imaging Systems and Technology. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE IMAGEM, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MELLO, Rodrigo Fernandes de et al. Image indexing and retrieval using an ART-2A neural network architecture. International Journal of Imaging Systems and Technology, v. 18, n. 2-3, p. 202-208, 2008Tradução . . Disponível em: https://doi.org/10.1002/ima.20149. Acesso em: 22 jun. 2024.
APA
Mello, R. F. de, Bueno, J. M., Senger, L. J., & Yang, L. T. (2008). Image indexing and retrieval using an ART-2A neural network architecture. International Journal of Imaging Systems and Technology, 18( 2-3), 202-208. doi:10.1002/ima.20149
NLM
Mello RF de, Bueno JM, Senger LJ, Yang LT. Image indexing and retrieval using an ART-2A neural network architecture [Internet]. International Journal of Imaging Systems and Technology. 2008 ; 18( 2-3): 202-208.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1002/ima.20149
Vancouver
Mello RF de, Bueno JM, Senger LJ, Yang LT. Image indexing and retrieval using an ART-2A neural network architecture [Internet]. International Journal of Imaging Systems and Technology. 2008 ; 18( 2-3): 202-208.[citado 2024 jun. 22 ] Available from: https://doi.org/10.1002/ima.20149
Trabalho de evento
Escalonamento de processos utilizando técnicas de ACO (2006)
Nery, Bruno Rodrigues; Mello, Rodrigo Fernandes de; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Anais. Conference titles: Congresso da Sociedade Brasileira de Computação - CSBC. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS
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ABNT
NERY, Bruno Rodrigues e MELLO, Rodrigo Fernandes de e CARVALHO, André Carlos Ponce de Leon Ferreira de. Escalonamento de processos utilizando técnicas de ACO. 2006, Anais.. Porto Alegre, RS: SBC, 2006. . Acesso em: 22 jun. 2024.
APA
Nery, B. R., Mello, R. F. de, & Carvalho, A. C. P. de L. F. de. (2006). Escalonamento de processos utilizando técnicas de ACO. In Anais. Porto Alegre, RS: SBC.
NLM
Nery BR, Mello RF de, Carvalho ACP de LF de. Escalonamento de processos utilizando técnicas de ACO. Anais. 2006 ;[citado 2024 jun. 22 ]
Vancouver
Nery BR, Mello RF de, Carvalho ACP de LF de. Escalonamento de processos utilizando técnicas de ACO. Anais. 2006 ;[citado 2024 jun. 22 ]

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