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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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
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 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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] 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: 03 nov. 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 nov. 03 ] 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 nov. 03 ] Available from: https://doi.org/10.24963/ijcai.2018/727

USP Schools

ReP

Filtros

Authors

Subjects