ReP USP - Resultado da busca

Artigo de periodico
The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference (2020)
Cozman, Fabio Gagliardi ; Mauá, Denis Deratani
Source: International Journal of Approximate Reasoning. Unidades: EP, IME
Subjects: PROGRAMAÇÃO LÓGICA, COMPUTABILIDADE E COMPLEXIDADE
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ABNT
COZMAN, Fabio Gagliardi e MAUÁ, Denis Deratani. The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference. International Journal of Approximate Reasoning, v. 125, p. 218-239, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.07.004. Acesso em: 01 maio 2024.
APA
Cozman, F. G., & Mauá, D. D. (2020). The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference. International Journal of Approximate Reasoning, 125, 218-239. doi:10.1016/j.ijar.2020.07.004
NLM
Cozman FG, Mauá DD. The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 218-239.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.004
Vancouver
Cozman FG, Mauá DD. The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 218-239.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.004
Artigo de periodico
Complexity results for probabilistic answer set programming (2020)
Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Assunto: COMPUTABILIDADE E COMPLEXIDADE
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ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. Complexity results for probabilistic answer set programming. International Journal of Approximate Reasoning, v. 118, p. 133-154, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2019.12.003. Acesso em: 01 maio 2024.
APA
Mauá, D. D., & Cozman, F. G. (2020). Complexity results for probabilistic answer set programming. International Journal of Approximate Reasoning, 118, 133-154. doi:10.1016/j.ijar.2019.12.003
NLM
Mauá DD, Cozman FG. Complexity results for probabilistic answer set programming [Internet]. International Journal of Approximate Reasoning. 2020 ;118 133-154.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2019.12.003
Vancouver
Mauá DD, Cozman FG. Complexity results for probabilistic answer set programming [Internet]. International Journal of Approximate Reasoning. 2020 ;118 133-154.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2019.12.003
Artigo de periodico
Thirty years of credal networks: specification, algorithms and complexity (2020)
Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, PROBABILIDADE
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ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. Thirty years of credal networks: specification, algorithms and complexity. International Journal of Approximate Reasoning, v. 126, p. 133-157, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.08.009. Acesso em: 01 maio 2024.
APA
Mauá, D. D., & Cozman, F. G. (2020). Thirty years of credal networks: specification, algorithms and complexity. International Journal of Approximate Reasoning, 126, 133-157. doi:10.1016/j.ijar.2020.08.009
NLM
Mauá DD, Cozman FG. Thirty years of credal networks: specification, algorithms and complexity [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 133-157.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
Vancouver
Mauá DD, Cozman FG. Thirty years of credal networks: specification, algorithms and complexity [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 133-157.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
Trabalho de evento
Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks (2020)
Deberaldini Netto, Caio Fabrício; Tannuri, Eduardo Aoun ; Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: Proceedings. Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. Unidades: EP, IME
Subjects: REDES NEURAIS, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
DEBERALDINI NETTO, Caio Fabrício et al. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11981. Acesso em: 01 maio 2024.
APA
Deberaldini Netto, C. F., Tannuri, E. A., Mauá, D. D., & Cozman, F. G. (2020). Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11981
NLM
Deberaldini Netto CF, Tannuri EA, Mauá DD, Cozman FG. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks [Internet]. Proceedings. 2020 ;[citado 2024 maio 01 ] Available from: https://doi.org/10.5753/kdmile.2020.11981
Vancouver
Deberaldini Netto CF, Tannuri EA, Mauá DD, Cozman FG. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks [Internet]. Proceedings. 2020 ;[citado 2024 maio 01 ] Available from: https://doi.org/10.5753/kdmile.2020.11981
Artigo de periodico
The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws (2019)
Cozman, Fabio Gagliardi ; Mauá, Denis Deratani
Source: International Journal of Approximate Reasoning. Unidades: EP, IME
Subjects: TEORIA DA COMPUTAÇÃO, TEORIA DOS MODELOS, AQUISIÇÃO DE CONHECIMENTO, LÓGICA MATEMÁTICA
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ABNT
COZMAN, Fabio Gagliardi e MAUÁ, Denis Deratani. The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws. International Journal of Approximate Reasoning, v. 110, p. 107-126, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2019.04.003. Acesso em: 01 maio 2024.
APA
Cozman, F. G., & Mauá, D. D. (2019). The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws. International Journal of Approximate Reasoning, 110, 107-126. doi:10.1016/j.ijar.2019.04.003
NLM
Cozman FG, Mauá DD. The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws [Internet]. International Journal of Approximate Reasoning. 2019 ; 110 107-126.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2019.04.003
Vancouver
Cozman FG, Mauá DD. The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws [Internet]. International Journal of Approximate Reasoning. 2019 ; 110 107-126.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2019.04.003
Artigo de periodico
Speeding up parameter and rule learning for acyclic probabilistic logic programs (2019)
Faria, Francisco Henrique Otte Vieira de ; Gusmão, Arthur Colombini ; De Bona, Glauber ; Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: International Journal of Approximate Reasoning. Unidades: EP, IME
Subjects: PROGRAMAÇÃO LÓGICA, APRENDIZADO COMPUTACIONAL
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ABNT
FARIA, Francisco Henrique Otte Vieira de et al. Speeding up parameter and rule learning for acyclic probabilistic logic programs. International Journal of Approximate Reasoning, v. 106, p. 32-50, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2018.12.012. Acesso em: 01 maio 2024.
APA
Faria, F. H. O. V. de, Gusmão, A. C., De Bona, G., Mauá, D. D., & Cozman, F. G. (2019). Speeding up parameter and rule learning for acyclic probabilistic logic programs. International Journal of Approximate Reasoning, 106, 32-50. doi:10.1016/j.ijar.2018.12.012
NLM
Faria FHOV de, Gusmão AC, De Bona G, Mauá DD, Cozman FG. Speeding up parameter and rule learning for acyclic probabilistic logic programs [Internet]. International Journal of Approximate Reasoning. 2019 ; 106 32-50.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2018.12.012
Vancouver
Faria FHOV de, Gusmão AC, De Bona G, Mauá DD, Cozman FG. Speeding up parameter and rule learning for acyclic probabilistic logic programs [Internet]. International Journal of Approximate Reasoning. 2019 ; 106 32-50.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2018.12.012
Artigo de periodico
The complexity of Bayesian networks specified by propositional and relational languages (2018)
Cozman, Fabio Gagliardi ; Mauá, Denis Deratani
Source: Artificial Intelligence. Unidades: EP, IME
Subjects: INFERÊNCIA BAYESIANA, COMPUTABILIDADE E COMPLEXIDADE
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ABNT
COZMAN, Fabio Gagliardi e MAUÁ, Denis Deratani. The complexity of Bayesian networks specified by propositional and relational languages. Artificial Intelligence, v. 262, p. 96-141, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.artint.2018.06.001. Acesso em: 01 maio 2024.
APA
Cozman, F. G., & Mauá, D. D. (2018). The complexity of Bayesian networks specified by propositional and relational languages. Artificial Intelligence, 262, 96-141. doi:10.1016/j.artint.2018.06.001
NLM
Cozman FG, Mauá DD. The complexity of Bayesian networks specified by propositional and relational languages [Internet]. Artificial Intelligence. 2018 ; 262 96-141.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.artint.2018.06.001
Vancouver
Cozman FG, Mauá DD. The complexity of Bayesian networks specified by propositional and relational languages [Internet]. Artificial Intelligence. 2018 ; 262 96-141.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.artint.2018.06.001
Artigo de periodico
Robustifying sum-product networks (2018)
Mauá, Denis Deratani ; Conaty, Diarmaid; Cozman, Fabio Gagliardi ; Poppenhaeger, Katja; Campos, Cassio Polpo de
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Subjects: INTELIGÊNCIA ARTIFICIAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS
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ABNT
MAUÁ, Denis Deratani et al. Robustifying sum-product networks. International Journal of Approximate Reasoning, v. 101, p. 163-180, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2018.07.003. Acesso em: 01 maio 2024.
APA
Mauá, D. D., Conaty, D., Cozman, F. G., Poppenhaeger, K., & Campos, C. P. de. (2018). Robustifying sum-product networks. International Journal of Approximate Reasoning, 101, 163-180. doi:10.1016/j.ijar.2018.07.003
NLM
Mauá DD, Conaty D, Cozman FG, Poppenhaeger K, Campos CP de. Robustifying sum-product networks [Internet]. International Journal of Approximate Reasoning. 2018 ; 101 163-180.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2018.07.003
Vancouver
Mauá DD, Conaty D, Cozman FG, Poppenhaeger K, Campos CP de. Robustifying sum-product networks [Internet]. International Journal of Approximate Reasoning. 2018 ; 101 163-180.[citado 2024 maio 01 ] Available from: https://doi.org/10.1016/j.ijar.2018.07.003
Trabalho de evento
The finite model theory of bayesian networks: descriptive complexity (2018)
Cozman, Fabio Gagliardi ; Mauá, Denis Deratani
Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidades: IME, EP
Subjects: COMPUTABILIDADE E COMPLEXIDADE, INTELIGÊNCIA ARTIFICIAL, INFERÊNCIA BAYESIANA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COZMAN, Fabio Gagliardi e MAUÁ, Denis Deratani. The finite model theory of bayesian networks: descriptive complexity. 2018, Anais.. Vienna: IJCAI, 2018. Disponível em: https://doi.org/10.24963/ijcai.2018/727. Acesso em: 01 maio 2024.
APA
Cozman, F. G., & Mauá, D. D. (2018). The finite model theory of bayesian networks: descriptive complexity. In Proceedings. Vienna: IJCAI. doi:10.24963/ijcai.2018/727
NLM
Cozman FG, Mauá DD. The finite model theory of bayesian networks: descriptive complexity [Internet]. Proceedings. 2018 ;[citado 2024 maio 01 ] Available from: https://doi.org/10.24963/ijcai.2018/727
Vancouver
Cozman FG, Mauá DD. The finite model theory of bayesian networks: descriptive complexity [Internet]. Proceedings. 2018 ;[citado 2024 maio 01 ] Available from: https://doi.org/10.24963/ijcai.2018/727

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