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
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. 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: 15 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_25NLM
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. 15 ] Available from: https://link.springer.com/chapter/10.1007/978-3-032-06118-8_25#Sec2Vancouver
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. 15 ] Available from: https://link.springer.com/chapter/10.1007/978-3-032-06118-8_25#Sec2
