Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging (2021)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; GEH, RENATO LUI - IME
- Unidade: IME
- Subjects: INTELIGÊNCIA ARTIFICIAL; APRENDIZADO COMPUTACIONAL; RACIOCÍNIO PROBABILÍSTICO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings of Machine Learning Research : PMLR
- ISSN: 2640-3498
- Volume/Número/Paginação/Ano: v. 161, p. 2039-2049, 2021
- Conference titles: Conference on Uncertainty in Artificial Intelligence - UAI
-
ABNT
GEH, Renato Lui e MAUÁ, Denis Deratani. Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging. Proceedings of Machine Learning Research : PMLR. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://proceedings.mlr.press/v161/geh21a/geh21a-supp.pdf. Acesso em: 15 out. 2024. , 2021 -
APA
Geh, R. L., & Mauá, D. D. (2021). Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging. Proceedings of Machine Learning Research : PMLR. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://proceedings.mlr.press/v161/geh21a/geh21a-supp.pdf -
NLM
Geh RL, Mauá DD. Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging [Internet]. Proceedings of Machine Learning Research : PMLR. 2021 ; 161 2039-2049.[citado 2024 out. 15 ] Available from: https://proceedings.mlr.press/v161/geh21a/geh21a-supp.pdf -
Vancouver
Geh RL, Mauá DD. Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging [Internet]. Proceedings of Machine Learning Research : PMLR. 2021 ; 161 2039-2049.[citado 2024 out. 15 ] Available from: https://proceedings.mlr.press/v161/geh21a/geh21a-supp.pdf - End-to-end imitation learning of lane following policies using sum-product networks
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