Mitigating data scarcity in polymer property prediction via multi-task auxiliary learning (2026)
- Authors:
- Autor USP: SILVA, JUAREZ LOPES FERREIRA DA - IQSC
- Unidade: IQSC
- DOI: 10.1007/978-3-032-06118-8_25
- Subjects: POLÍMEROS (MATERIAIS); APRENDIZADO COMPUTACIONAL; MATERIAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 06 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. 06 ] 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. 06 ] Available from: https://link.springer.com/chapter/10.1007/978-3-032-06118-8_25#Sec2 - Hybrid density functional study of small Rhn (n = 2−15) clusters
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Informações sobre o DOI: 10.1007/978-3-032-06118-8_25 (Fonte: oaDOI API)
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