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Parte de monografia/livro
Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning (2026)
Pinheiro, Gabriel A. ; Quiles, Marcos Gonçalves ; Silva, Juarez Lopes Ferreira da ; Fern, Xiaoli Z
Fonte: Machine learning and knowledge discovery in databases.Applied Data Science Track. ECML PKDD 2025. Lecture Notes in Computer Science.. Unidade: IQSC
Assuntos: POLÍMEROS (MATERIAIS), APRENDIZADO COMPUTACIONAL, MATERIAIS
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ABNT
PINHEIRO, Gabriel A. et al. Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning. Machine learning and knowledge discovery in databases.Applied Data Science Track. ECML PKDD 2025. Lecture Notes in Computer Science. Tradução . Cham: Springer, 2026. . Disponível em: https://link.springer.com/chapter/10.1007/978-3-032-06118-8_25#Sec2. Acesso em: 03 dez. 2025.
APA
Pinheiro, G. A., Quiles, M. G., Silva, J. L. F. da, & Fern, X. Z. (2026). Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning. In Machine learning and knowledge discovery in databases.Applied Data Science Track. ECML PKDD 2025. Lecture Notes in Computer Science.. Cham: Springer. doi:10.1007/978-3-032-06118-8_25
NLM
Pinheiro GA, Quiles MG, Silva JLF da, Fern XZ. Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning [Internet]. In: Machine learning and knowledge discovery in databases.Applied Data Science Track. ECML PKDD 2025. Lecture Notes in Computer Science. Cham: Springer; 2026. [citado 2025 dez. 03 ] Available from: https://link.springer.com/chapter/10.1007/978-3-032-06118-8_25#Sec2
Vancouver
Pinheiro GA, Quiles MG, Silva JLF da, Fern XZ. Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning [Internet]. In: Machine learning and knowledge discovery in databases.Applied Data Science Track. ECML PKDD 2025. Lecture Notes in Computer Science. Cham: Springer; 2026. [citado 2025 dez. 03 ] Available from: https://link.springer.com/chapter/10.1007/978-3-032-06118-8_25#Sec2
Trabalho de evento
Enhancing low-cost molecular property prediction with contrastive learning on SMILES representations (2024)
Quiles, Marcos G. ; Ribeiro, Piero A. L. ; Pinheiro, Gabriel A. ; Prati, Ronaldo ; Silva, Juarez L. F. da
Fonte: Proceedings. Nome do evento: International Conference on Computational Science and Its Applications. Unidade: IQSC
Assuntos: MOLÉCULA, APRENDIZADO COMPUTACIONAL, CIÊNCIA DA COMPUTAÇÃO
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ABNT
QUILES, Marcos G. et al. Enhancing low-cost molecular property prediction with contrastive learning on SMILES representations. 2024, Anais.. Cham: Springer, 2024. Disponível em: https://doi.org/10.1007/978-3-031-65329-2_26. Acesso em: 03 dez. 2025.
APA
Quiles, M. G., Ribeiro, P. A. L., Pinheiro, G. A., Prati, R., & Silva, J. L. F. da. (2024). Enhancing low-cost molecular property prediction with contrastive learning on SMILES representations. In Proceedings. Cham: Springer. doi:10.1007/978-3-031-65329-2_26
NLM
Quiles MG, Ribeiro PAL, Pinheiro GA, Prati R, Silva JLF da. Enhancing low-cost molecular property prediction with contrastive learning on SMILES representations [Internet]. Proceedings. 2024 ;[citado 2025 dez. 03 ] Available from: https://doi.org/10.1007/978-3-031-65329-2_26
Vancouver
Quiles MG, Ribeiro PAL, Pinheiro GA, Prati R, Silva JLF da. Enhancing low-cost molecular property prediction with contrastive learning on SMILES representations [Internet]. Proceedings. 2024 ;[citado 2025 dez. 03 ] Available from: https://doi.org/10.1007/978-3-031-65329-2_26
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.
Fonte: Corvallis : College of Engineering Oregon State University, 2024. Nome do evento: Chemical, Biological, and Environmental Engineering Seminar - CBEE. Unidade: IQSC
Assuntos: POLÍMEROS (MATERIAIS), APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 dez. 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 dez. 03 ] 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 dez. 03 ] Available from: https://repositorio.usp.br/directbitstream/6d62f8cb-1d90-47c2-bf5f-e454e6e85238/P22205.pdf
Artigo de periodico
SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning (2022)
Pinheiro, Gabriel A.; Silva, Juarez Lopes Ferreira da ; Quiles, Marcos Gonçalves
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: MODELAGEM MOLECULAR, MOLÉCULA, QUÍMICA TEÓRICA
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ABNT
PINHEIRO, Gabriel A. e SILVA, Juarez Lopes Ferreira da e QUILES, Marcos Gonçalves. SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning. Journal of Chemical Information and Modeling, v. 62, n. 17, p. 3948–3960, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00521. Acesso em: 03 dez. 2025.
APA
Pinheiro, G. A., Silva, J. L. F. da, & Quiles, M. G. (2022). SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning. Journal of Chemical Information and Modeling, 62( 17), 3948–3960. doi:10.1021/acs.jcim.2c00521
NLM
Pinheiro GA, Silva JLF da, Quiles MG. SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 17): 3948–3960.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1021/acs.jcim.2c00521
Vancouver
Pinheiro GA, Silva JLF da, Quiles MG. SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 17): 3948–3960.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1021/acs.jcim.2c00521
Artigo de periodico
Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining (2022)
Moraes, Alex S.; Pinheiro, Gabriel A.; Lourenço, Tuanan da Costa ; Lopes, Mauro C.; Quiles, Marcos G.; Dias, Luis G. ; Silva, Juarez Lopes Ferreira da
Fonte: Journal of Chemical Information and Modeling. Unidades: FFCLRP, IQSC
Assuntos: ÍONS ELETRÔNICOS, ESTRUTURA ATÔMICA (QUÍMICA TEÓRICA), ENERGIA
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ABNT
MORAES, Alex S. et al. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining. Journal of Chemical Information and Modeling, v. 62, n. 19, p. 4702–4712, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00748. Acesso em: 03 dez. 2025.
APA
Moraes, A. S., Pinheiro, G. A., Lourenço, T. da C., Lopes, M. C., Quiles, M. G., Dias, L. G., & Silva, J. L. F. da. (2022). Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining. Journal of Chemical Information and Modeling, 62( 19), 4702–4712. doi:10.1021/acs.jcim.2c00748
NLM
Moraes AS, Pinheiro GA, Lourenço T da C, Lopes MC, Quiles MG, Dias LG, Silva JLF da. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4702–4712.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1021/acs.jcim.2c00748
Vancouver
Moraes AS, Pinheiro GA, Lourenço T da C, Lopes MC, Quiles MG, Dias LG, Silva JLF da. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4702–4712.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1021/acs.jcim.2c00748
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.
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: 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: 03 dez. 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 dez. 03 ] 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 dez. 03 ] Available from: https://doi.org/10.1021/acs.jcim.1c00503
Artigo de periodico
Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset (2020)
Pinheiro, Gabriel A.; Mucelini, Johnatan ; Soares, Marinalva Dias ; Prati, Ronaldo Cristiano ; Silva, Juarez Lopes Ferreira da ; Quiles, Marcos Gonçalves
Fonte: Journal of Physical Chemistry A. Unidades: IQSC, ICMC
Assunto: QUÍMICA QUÂNTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PINHEIRO, Gabriel A. et al. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset. Journal of Physical Chemistry A, v. No 2020, n. 47, p. 9854–9866, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jpca.0c05969. Acesso em: 03 dez. 2025.
APA
Pinheiro, G. A., Mucelini, J., Soares, M. D., Prati, R. C., Silva, J. L. F. da, & Quiles, M. G. (2020). Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset. Journal of Physical Chemistry A, No 2020( 47), 9854–9866. doi:10.1021/acs.jpca.0c05969
NLM
Pinheiro GA, Mucelini J, Soares MD, Prati RC, Silva JLF da, Quiles MG. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset [Internet]. Journal of Physical Chemistry A. 2020 ; No 2020( 47): 9854–9866.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1021/acs.jpca.0c05969
Vancouver
Pinheiro GA, Mucelini J, Soares MD, Prati RC, Silva JLF da, Quiles MG. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset [Internet]. Journal of Physical Chemistry A. 2020 ; No 2020( 47): 9854–9866.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1021/acs.jpca.0c05969

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