Two reformulation approaches to maximum-a-posteriori inference in sum-product networks (2020)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; RIBEIRO, HEITOR REIS - IME
- Unidade: IME
- Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS; PROGRAMAÇÃO LINEAR
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings of Machine Learning Research
- ISSN: 2640-3498
- Volume/Número/Paginação/Ano: v. 138, p. 293-304, 2020
- Conference titles: International Conference on Probabilistic Graphical Models
-
ABNT
MAUÁ, Denis Deratani et al. Two reformulation approaches to maximum-a-posteriori inference in sum-product networks. Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://proceedings.mlr.press/v138/maua20a/maua20a.pdf. Acesso em: 21 jan. 2026. , 2020 -
APA
Mauá, D. D., Ribeiro, H. R., Katague, G. P., & Antonucci, A. (2020). Two reformulation approaches to maximum-a-posteriori inference in sum-product networks. Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://proceedings.mlr.press/v138/maua20a/maua20a.pdf -
NLM
Mauá DD, Ribeiro HR, Katague GP, Antonucci A. Two reformulation approaches to maximum-a-posteriori inference in sum-product networks [Internet]. Proceedings of Machine Learning Research. 2020 ; 138 293-304.[citado 2026 jan. 21 ] Available from: https://proceedings.mlr.press/v138/maua20a/maua20a.pdf -
Vancouver
Mauá DD, Ribeiro HR, Katague GP, Antonucci A. Two reformulation approaches to maximum-a-posteriori inference in sum-product networks [Internet]. Proceedings of Machine Learning Research. 2020 ; 138 293-304.[citado 2026 jan. 21 ] Available from: https://proceedings.mlr.press/v138/maua20a/maua20a.pdf - A benchmark for Maximum-a-Posteriori Inference algorithms in discrete Sum-Product Networks
- The National Meeting on Artificial and Computational Intelligence (ENIAC) is one the main national forums for researchers... [Preface]
- Approximation complexity of maximum a posteriori inference in sum-product networks
- Hidden Markov models with set-valued parameters
- Modelos de tópicos na classificação automática de resenhas de usuário
- Advances in automatically solving the ENEM
- Advances in learning Bayesian networks of bounded treewidth
- Better initialization heuristics for order-based bayesian network structure learning
- Time robust trees: using temporal invariance to improve generalization
- Special issue on robustness in probabilistic graphical models. [Editorial]
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