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Filtros : "Financiamento Shell" "Pinheiro, Gabriel A." Limpar

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  • Trabalho de evento

    Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning (2024)

    Pinheiro, Gabriel A. ; Quiles, Marcos Gonçalves ; Silva, Juarez Lopes Ferreira da ; Fern, Xiaoli Z.

    Source: Corvallis : College of Engineering Oregon State University, 2024. Conference titles: Chemical, Biological, and Environmental Engineering Seminar - CBEE. Unidade: IQSC

    Subjects: POLÍMEROS (MATERIAIS), APRENDIZADO COMPUTACIONAL

    Versão PublicadaHow to cite
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    • ABNT

      PINHEIRO, Gabriel A. et al. Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning. 2024, Anais.. Corvallis: Instituto de Química de São Carlos, Universidade de São Paulo, 2024. Disponível em: https://repositorio.usp.br/directbitstream/6d62f8cb-1d90-47c2-bf5f-e454e6e85238/P22205.pdf. Acesso em: 09 nov. 2025.

    • APA

      Pinheiro, G. A., Quiles, M. G., Silva, J. L. F. da, & Fern, X. Z. (2024). Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning. In Corvallis : College of Engineering Oregon State University, 2024. Corvallis: Instituto de Química de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/6d62f8cb-1d90-47c2-bf5f-e454e6e85238/P22205.pdf

    • NLM

      Pinheiro GA, Quiles MG, Silva JLF da, Fern XZ. Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning [Internet]. Corvallis : College of Engineering Oregon State University, 2024. 2024 ;[citado 2025 nov. 09 ] Available from: https://repositorio.usp.br/directbitstream/6d62f8cb-1d90-47c2-bf5f-e454e6e85238/P22205.pdf

    • Vancouver

      Pinheiro GA, Quiles MG, Silva JLF da, Fern XZ. Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning [Internet]. Corvallis : College of Engineering Oregon State University, 2024. 2024 ;[citado 2025 nov. 09 ] Available from: https://repositorio.usp.br/directbitstream/6d62f8cb-1d90-47c2-bf5f-e454e6e85238/P22205.pdf

  • Artigo de periodico

    Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition (2021)

    Azevedo, Luis Cesar de; Pinheiro, Gabriel A.; Quiles, Marcos G.; Silva, Juarez Lopes Ferreira da ; Prati, Ronaldo C.

    Source: Journal of Chemical Information and Modeling. Unidade: IQSC

    Subjects: QUÍMICA QUÂNTICA, ALGORITMOS

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    • ABNT

      AZEVEDO, Luis Cesar de et al. Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition. Journal of Chemical Information and Modeling, v. 61, p. 4210−4223, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00503. Acesso em: 09 nov. 2025.

    • APA

      Azevedo, L. C. de, Pinheiro, G. A., Quiles, M. G., Silva, J. L. F. da, & Prati, R. C. (2021). Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition. Journal of Chemical Information and Modeling, 61, 4210−4223. doi:10.1021/acs.jcim.1c00503

    • NLM

      Azevedo LC de, Pinheiro GA, Quiles MG, Silva JLF da, Prati RC. Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 4210−4223.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00503

    • Vancouver

      Azevedo LC de, Pinheiro GA, Quiles MG, Silva JLF da, Prati RC. Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 4210−4223.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00503
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