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  • 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: 24 abr. 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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.004
  • 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: 24 abr. 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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.1016/j.ijar.2019.12.003
  • 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: 24 abr. 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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
  • 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: 24 abr. 2024.
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      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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.5753/kdmile.2020.11981
  • 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: 24 abr. 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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.1016/j.ijar.2019.04.003
  • 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: 24 abr. 2024.
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      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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.1016/j.ijar.2018.12.012
  • Source: Artificial Intelligence. Unidades: EP, IME

    Subjects: INFERÊNCIA BAYESIANA, COMPUTABILIDADE E COMPLEXIDADE

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      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: 24 abr. 2024.
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      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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.1016/j.artint.2018.06.001
  • Source: International Journal of Approximate Reasoning. Unidades: IME, EP

    Subjects: INTELIGÊNCIA ARTIFICIAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS

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      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: 24 abr. 2024.
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      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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.1016/j.ijar.2018.07.003
  • Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidades: IME, EP

    Subjects: COMPUTABILIDADE E COMPLEXIDADE, INTELIGÊNCIA ARTIFICIAL, INFERÊNCIA BAYESIANA

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    • 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: 24 abr. 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 abr. 24 ] 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 abr. 24 ] Available from: https://doi.org/10.24963/ijcai.2018/727

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