Learning probabilistic sentential decision diagrams by sampling (2020)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; GEH, RENATO LUI - IME
- Unidade: IME
- DOI: 10.5753/kdmile.2020.11968
- Subjects: INTELIGÊNCIA ARTIFICIAL; APRENDIZADO COMPUTACIONAL; RACIOCÍNIO PROBABILÍSTICO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2020
- Source:
- Título do periódico: Proceedings
- Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: bronze
-
ABNT
GEH, Renato Lui e MAUÁ, Denis Deratani e ANTONUCCI, Alessandro. Learning probabilistic sentential decision diagrams by sampling. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11968. Acesso em: 20 abr. 2024. -
APA
Geh, R. L., Mauá, D. D., & Antonucci, A. (2020). Learning probabilistic sentential decision diagrams by sampling. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11968 -
NLM
Geh RL, Mauá DD, Antonucci A. Learning probabilistic sentential decision diagrams by sampling [Internet]. Proceedings. 2020 ;[citado 2024 abr. 20 ] Available from: https://doi.org/10.5753/kdmile.2020.11968 -
Vancouver
Geh RL, Mauá DD, Antonucci A. Learning probabilistic sentential decision diagrams by sampling [Internet]. Proceedings. 2020 ;[citado 2024 abr. 20 ] Available from: https://doi.org/10.5753/kdmile.2020.11968 - End-to-end imitation learning of lane following policies using sum-product networks
- Scalable learning of probabilistic circuits
- Fast and accurate learning of probabilistic circuits by random projections
- Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging
- Scalable learning of probabilistic circuits
- Hidden Markov models with set-valued parameters
- Advances in learning Bayesian networks of bounded treewidth
- Early classification of time series by Hidden Markov Models with set-valued parameters
- Initialization heuristics for greedy bayesian network structure learning
- Time robust trees: using temporal invariance to improve generalization
Informações sobre o DOI: 10.5753/kdmile.2020.11968 (Fonte: oaDOI API)
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