Filtros : "COSTA, ANNA HELENA REALI" Limpar

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  • Source: IEEE Access. Unidade: EP

    Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS

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      COTRIM, LUCAS P. et al. Neural network meta-models for FPSO motion prediction from environmental data with different platform loads. IEEE Access, v. 10, p. 86558-86577, 2022Tradução . . Disponível em: Digital Object Identifier 10.1109/ACCESS.2022.3199009. Acesso em: 04 fev. 2023.
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      COTRIM, L. U. C. A. S. P., Barreira, R. A., Santos, I. H. F. dos, Gomi, E. S., Costa, A. H. R., & Tannuri, E. A. (2022). Neural network meta-models for FPSO motion prediction from environmental data with different platform loads. IEEE Access, 10, 86558-86577. doi:10.1109/ACCESS.2022.3199009
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      COTRIM LUCASP, Barreira RA, Santos IHF dos, Gomi ES, Costa AHR, Tannuri EA. Neural network meta-models for FPSO motion prediction from environmental data with different platform loads [Internet]. IEEE Access. 2022 ;10 86558-86577.[citado 2023 fev. 04 ] Available from: Digital Object Identifier 10.1109/ACCESS.2022.3199009
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      COTRIM LUCASP, Barreira RA, Santos IHF dos, Gomi ES, Costa AHR, Tannuri EA. Neural network meta-models for FPSO motion prediction from environmental data with different platform loads [Internet]. IEEE Access. 2022 ;10 86558-86577.[citado 2023 fev. 04 ] Available from: Digital Object Identifier 10.1109/ACCESS.2022.3199009
  • Source: Expert Systems with Applications. Unidade: EP

    Assunto: APRENDIZADO COMPUTACIONAL

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      FELIZARDO, Leonardo Kanashiro et al. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market. Expert Systems with Applications, v. 202, p. 117259, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2022.117259. Acesso em: 04 fev. 2023.
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      Felizardo, L. K., Brandimarte, P., Del Moral Hernandez, E., Costa, A. H. R., Matsumoto, E. Y., Paiva, F. C. L., & Graves, C. de V. (2022). Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market. Expert Systems with Applications, 202, 117259. doi:10.1016/j.eswa.2022.117259
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      Felizardo LK, Brandimarte P, Del Moral Hernandez E, Costa AHR, Matsumoto EY, Paiva FCL, Graves C de V. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market [Internet]. Expert Systems with Applications. 2022 ;202 117259.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.eswa.2022.117259
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      Felizardo LK, Brandimarte P, Del Moral Hernandez E, Costa AHR, Matsumoto EY, Paiva FCL, Graves C de V. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market [Internet]. Expert Systems with Applications. 2022 ;202 117259.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.eswa.2022.117259
  • Source: Journal of Control, Automation and Electrical Systems. Unidade: EP

    Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, ROBÓTICA

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      SILVA, Isaac José da et al. Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900. Journal of Control, Automation and Electrical Systems, v. 32, p. 884–894, 2021Tradução . . Disponível em: https://doi.org/10.1007/s40313-021-00719-8. Acesso em: 04 fev. 2023.
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      Silva, I. J. da, Vilão Junior, C. O., Costa, A. H. R., & Bianchi, R. A. da C. (2021). Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900. Journal of Control, Automation and Electrical Systems, 32, 884–894. doi:10.1007/s40313-021-00719-8
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      Silva IJ da, Vilão Junior CO, Costa AHR, Bianchi RA da C. Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900 [Internet]. Journal of Control, Automation and Electrical Systems. 2021 ;32 884–894.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1007/s40313-021-00719-8
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      Silva IJ da, Vilão Junior CO, Costa AHR, Bianchi RA da C. Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900 [Internet]. Journal of Control, Automation and Electrical Systems. 2021 ;32 884–894.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1007/s40313-021-00719-8
  • Source: IEEE Access. Unidade: EP

    Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, AMARRAÇÃO, CABOS DE AMARRAÇÃO, ESTRUTURAS FLUTUANTES

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      SAAD, Amir Muhammed et al. Using neural network approaches to detect mooring line failure. IEEE Access, v. fe 2021, p. 27678-27695, 2021Tradução . . Disponível em: https://repositorio.usp.br/directbitstream/b872384c-71c9-40e6-abf5-a8ebc82c814e/Tannuri-2021-Using%20neural%20network%20approaches%20to%20detect%20mooring%20line%20failure.pdf. Acesso em: 04 fev. 2023.
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      Saad, A. M., Schopp, F., Barreira, R. A., Santos, I. H. F. dos, Tannuri, E. A., Gomi, E. S., & Reali Costa, A. H. (2021). Using neural network approaches to detect mooring line failure. IEEE Access, fe 2021, 27678-27695. doi:10.1109/ACCESS.2021.3058592
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      Saad AM, Schopp F, Barreira RA, Santos IHF dos, Tannuri EA, Gomi ES, Reali Costa AH. Using neural network approaches to detect mooring line failure [Internet]. IEEE Access. 2021 ; fe 2021 27678-27695.[citado 2023 fev. 04 ] Available from: https://repositorio.usp.br/directbitstream/b872384c-71c9-40e6-abf5-a8ebc82c814e/Tannuri-2021-Using%20neural%20network%20approaches%20to%20detect%20mooring%20line%20failure.pdf
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      Saad AM, Schopp F, Barreira RA, Santos IHF dos, Tannuri EA, Gomi ES, Reali Costa AH. Using neural network approaches to detect mooring line failure [Internet]. IEEE Access. 2021 ; fe 2021 27678-27695.[citado 2023 fev. 04 ] Available from: https://repositorio.usp.br/directbitstream/b872384c-71c9-40e6-abf5-a8ebc82c814e/Tannuri-2021-Using%20neural%20network%20approaches%20to%20detect%20mooring%20line%20failure.pdf
  • Source: Proceedings. Conference titles: ACM International Conference on Information and Knowledge Management - CIKM. Unidades: EACH, EP

    Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, TRADUÇÃO AUTOMÁTICA, COMPUTAÇÃO APLICADA

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      PASCHOAL, André Faleiros Alves et al. Pirá: a bilingual portuguese-english dataset for question-answering about the ocean. 2021, Anais.. Aachen: CEUR-WS, 2021. Disponível em: https://doi.org/10.1145/3459637.3482012. Acesso em: 04 fev. 2023.
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      Paschoal, A. F. A., Pirozelli, P., Silva, V. F. da, Delgado, K. V., Peres, S. M., José, M. M., et al. (2021). Pirá: a bilingual portuguese-english dataset for question-answering about the ocean. In Proceedings. Aachen: CEUR-WS. doi:10.1145/3459637.3482012
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      Paschoal AFA, Pirozelli P, Silva VF da, Delgado KV, Peres SM, José MM, Nakasato F, Oliveira AS, Brandão AAF, Costa AHR, Cozman FG. Pirá: a bilingual portuguese-english dataset for question-answering about the ocean [Internet]. Proceedings. 2021 ;[citado 2023 fev. 04 ] Available from: https://doi.org/10.1145/3459637.3482012
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      Paschoal AFA, Pirozelli P, Silva VF da, Delgado KV, Peres SM, José MM, Nakasato F, Oliveira AS, Brandão AAF, Costa AHR, Cozman FG. Pirá: a bilingual portuguese-english dataset for question-answering about the ocean [Internet]. Proceedings. 2021 ;[citado 2023 fev. 04 ] Available from: https://doi.org/10.1145/3459637.3482012
  • Source: Inteligência artificial : avanços e tendências. Unidades: EP, IME, ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      COSTA, Anna Helena Reali et al. Trajetória acadêmica da inteligência artificial no Brasil. Inteligência artificial : avanços e tendências. Tradução . São Paulo: Instituto de Estudos Avançados, 2021. . Disponível em: https://doi.org/10.11606/9786587773131. Acesso em: 04 fev. 2023.
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      Costa, A. H. R., Barros, L. N. de, Rezende, S. O., Sichman, J. S., & Munhoz, H. N. (2021). Trajetória acadêmica da inteligência artificial no Brasil. In Inteligência artificial : avanços e tendências. São Paulo: Instituto de Estudos Avançados. doi:10.11606/9786587773131
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      Costa AHR, Barros LN de, Rezende SO, Sichman JS, Munhoz HN. Trajetória acadêmica da inteligência artificial no Brasil [Internet]. In: Inteligência artificial : avanços e tendências. São Paulo: Instituto de Estudos Avançados; 2021. [citado 2023 fev. 04 ] Available from: https://doi.org/10.11606/9786587773131
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      Costa AHR, Barros LN de, Rezende SO, Sichman JS, Munhoz HN. Trajetória acadêmica da inteligência artificial no Brasil [Internet]. In: Inteligência artificial : avanços e tendências. São Paulo: Instituto de Estudos Avançados; 2021. [citado 2023 fev. 04 ] Available from: https://doi.org/10.11606/9786587773131
  • Source: IEE ACCESS. Unidade: EP

    Assunto: APRENDIZADO COMPUTACIONAL

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      AMENDOLA, Jose et al. Navigation in Restricted Channels Under Environmental Conditions: Fast-Time Simulation by Asynchronous Deep Reinforcement Learning. IEE ACCESS, v. 8, p. 149199, 2020Tradução . . Disponível em: Digital Object Identifier 10.1109/ACCESS.2020.3015661. Acesso em: 04 fev. 2023.
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      Amendola, J., Miura, L. S., Costa, A. H. R., Cozman, F. G., & Tannuri, E. A. (2020). Navigation in Restricted Channels Under Environmental Conditions: Fast-Time Simulation by Asynchronous Deep Reinforcement Learning. IEE ACCESS, 8, 149199. doi:10.1109/ACCESS.2020.3015661
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      Amendola J, Miura LS, Costa AHR, Cozman FG, Tannuri EA. Navigation in Restricted Channels Under Environmental Conditions: Fast-Time Simulation by Asynchronous Deep Reinforcement Learning [Internet]. IEE ACCESS. 2020 ;8 149199.[citado 2023 fev. 04 ] Available from: Digital Object Identifier 10.1109/ACCESS.2020.3015661
    • Vancouver

      Amendola J, Miura LS, Costa AHR, Cozman FG, Tannuri EA. Navigation in Restricted Channels Under Environmental Conditions: Fast-Time Simulation by Asynchronous Deep Reinforcement Learning [Internet]. IEE ACCESS. 2020 ;8 149199.[citado 2023 fev. 04 ] Available from: Digital Object Identifier 10.1109/ACCESS.2020.3015661
  • Source: Artificial Intelligence. Unidade: EP

    Subjects: ROBÓTICA, APRENDIZADO COMPUTACIONAL

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      HOMEM, Thiago Pedro Donadon et al. Qualitative case-based reasoning and learning. Artificial Intelligence, v. 283, p. 103258, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.artint.2020.103258. Acesso em: 04 fev. 2023.
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      Homem, T. P. D., Costa, A. H. R., Bianchi, R. A. da C., Santos, P. E., & López de Mantaras, R. (2020). Qualitative case-based reasoning and learning. Artificial Intelligence, 283, 103258. doi:10.1016/j.artint.2020.103258
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      Homem TPD, Costa AHR, Bianchi RA da C, Santos PE, López de Mantaras R. Qualitative case-based reasoning and learning [Internet]. Artificial Intelligence. 2020 ;283 103258.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.artint.2020.103258
    • Vancouver

      Homem TPD, Costa AHR, Bianchi RA da C, Santos PE, López de Mantaras R. Qualitative case-based reasoning and learning [Internet]. Artificial Intelligence. 2020 ;283 103258.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.artint.2020.103258
  • Source: Expert Systems with Applications. Unidade: EP

    Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL

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      GLATT, Ruben et al. DECAF: deep case-based policy inference for knowledge transfer in reinforcement learning. Expert Systems with Applications, v. 156, p. 113420, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2020.113420. Acesso em: 04 fev. 2023.
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      Glatt, R., Silva, F. L. da, Bianchi, R. A. da C., & Costa, A. H. R. (2020). DECAF: deep case-based policy inference for knowledge transfer in reinforcement learning. Expert Systems with Applications, 156, 113420. doi:10.1016/j.eswa.2020.113420
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      Glatt R, Silva FL da, Bianchi RA da C, Costa AHR. DECAF: deep case-based policy inference for knowledge transfer in reinforcement learning [Internet]. Expert Systems with Applications. 2020 ; 156 113420.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.eswa.2020.113420
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      Glatt R, Silva FL da, Bianchi RA da C, Costa AHR. DECAF: deep case-based policy inference for knowledge transfer in reinforcement learning [Internet]. Expert Systems with Applications. 2020 ; 156 113420.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.eswa.2020.113420
  • Source: Expert Systems with Applications. Unidade: EP

    Assunto: ALGORITMOS

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      BERTANI, Ricardo Mitollo e BIANCHI, Reinaldo Augusto da Costa e REALI COSTA, Anna Helena. Combining novelty and popularity on personalised recommendations via user profile learning. Expert Systems with Applications, v. 146, p. 113149, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2019.113149. Acesso em: 04 fev. 2023.
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      Bertani, R. M., Bianchi, R. A. da C., & Reali Costa, A. H. (2020). Combining novelty and popularity on personalised recommendations via user profile learning. Expert Systems with Applications, 146, 113149. doi:10.1016/j.eswa.2019.113149
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      Bertani RM, Bianchi RA da C, Reali Costa AH. Combining novelty and popularity on personalised recommendations via user profile learning [Internet]. Expert Systems with Applications. 2020 ; 146 113149.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.eswa.2019.113149
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      Bertani RM, Bianchi RA da C, Reali Costa AH. Combining novelty and popularity on personalised recommendations via user profile learning [Internet]. Expert Systems with Applications. 2020 ; 146 113149.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.eswa.2019.113149
  • Source: Autonomous Agents and Multi-Agent Systems. Unidade: EP

    Subjects: SISTEMAS MULTIAGENTES, APRENDIZAO COMPUTACIONAL

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      SILVA, Felipe Leno da et al. Agents teaching agents: a survey on inter-agent transfer learning. Autonomous Agents and Multi-Agent Systems, v. 34, n. 9, p. 1-17, 2020Tradução . . Disponível em: https://doi.org/10.1007/s10458-019-09430-0. Acesso em: 04 fev. 2023.
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      Silva, F. L. da, Reali Costa, A. H., Stone, P., & Warnell, G. (2020). Agents teaching agents: a survey on inter-agent transfer learning. Autonomous Agents and Multi-Agent Systems, 34( 9), 1-17. doi:10.1007/s10458-019-09430-0
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      Silva FL da, Reali Costa AH, Stone P, Warnell G. Agents teaching agents: a survey on inter-agent transfer learning [Internet]. Autonomous Agents and Multi-Agent Systems. 2020 ; 34( 9): 1-17.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1007/s10458-019-09430-0
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      Silva FL da, Reali Costa AH, Stone P, Warnell G. Agents teaching agents: a survey on inter-agent transfer learning [Internet]. Autonomous Agents and Multi-Agent Systems. 2020 ; 34( 9): 1-17.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1007/s10458-019-09430-0
  • Source: EEE TRANSACTIONS ON SMART GRID. Unidade: EP

    Subjects: SISTEMAS MULTIAGENTES, APRENDIZADO COMPUTACIONAL

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      SILVA, Felipe Leno da et al. Coordination of electric vehicle charging through multiagent reinforcement learning. EEE TRANSACTIONS ON SMART GRID, v. 11, p. 2347-2356, 2020Tradução . . Disponível em: http://www.ieee.org/publications_standards/publications/rights/index.html for more information. Acesso em: 04 fev. 2023.
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      Silva, F. L. da, Nishida, C. E. H., ROIJERS, D. I. E. D. E. R. I. K. M., & Costa, A. H. R. (2020). Coordination of electric vehicle charging through multiagent reinforcement learning. EEE TRANSACTIONS ON SMART GRID, 11, 2347-2356. doi:10.1109/TSG.2019.2952331
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      Silva FL da, Nishida CEH, ROIJERS DIEDERIKM, Costa AHR. Coordination of electric vehicle charging through multiagent reinforcement learning [Internet]. EEE TRANSACTIONS ON SMART GRID. 2020 ;11 2347-2356.[citado 2023 fev. 04 ] Available from: http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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      Silva FL da, Nishida CEH, ROIJERS DIEDERIKM, Costa AHR. Coordination of electric vehicle charging through multiagent reinforcement learning [Internet]. EEE TRANSACTIONS ON SMART GRID. 2020 ;11 2347-2356.[citado 2023 fev. 04 ] Available from: http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
  • Source: Artificial Intelligence in Medicine. Unidade: EP

    Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS

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      NISHIDA, Cyntia Eico Hayama e BIANCHI, Reinaldo Augusto da Costa e COSTA, Anna Helena Reali. A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning. Artificial Intelligence in Medicine, v. 107, p. 101853, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.artmed.2020.101853. Acesso em: 04 fev. 2023.
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      Nishida, C. E. H., Bianchi, R. A. da C., & Costa, A. H. R. (2020). A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning. Artificial Intelligence in Medicine, 107, 101853. doi:10.1016/j.artmed.2020.101853
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      Nishida CEH, Bianchi RA da C, Costa AHR. A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning [Internet]. Artificial Intelligence in Medicine. 2020 ; 107 101853.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.artmed.2020.101853
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      Nishida CEH, Bianchi RA da C, Costa AHR. A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning [Internet]. Artificial Intelligence in Medicine. 2020 ; 107 101853.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.artmed.2020.101853
  • Source: IEEE Transactions on Cybernetics. Unidade: EP

    Assunto: APRENDIZADO COMPUTACIONAL

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      SILVA, Felipe Leno da e REALI COSTA, Anna Helena e GLATT, Ruben. MOO-MDP: an Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning. IEEE Transactions on Cybernetics, v. 49 , n. 2, p. 567-579, 2019Tradução . . Disponível em: https://doi.org/10.1109/TCYB.2017.2781130. Acesso em: 04 fev. 2023.
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      Silva, F. L. da, Reali Costa, A. H., & Glatt, R. (2019). MOO-MDP: an Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning. IEEE Transactions on Cybernetics, 49 ( 2), 567-579. doi:10.1109/TCYB.2017.2781130
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      Silva FL da, Reali Costa AH, Glatt R. MOO-MDP: an Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning [Internet]. IEEE Transactions on Cybernetics. 2019 ; 49 ( 2): 567-579.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1109/TCYB.2017.2781130
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      Silva FL da, Reali Costa AH, Glatt R. MOO-MDP: an Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning [Internet]. IEEE Transactions on Cybernetics. 2019 ; 49 ( 2): 567-579.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1109/TCYB.2017.2781130
  • Source: Journal of Artificial Intelligence Research. Unidade: EP

    Assunto: APRENDIZADO COMPUTACIONAL

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      SILVA, Felipe Leno da e REALI COSTA, Anna Helena. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems. Journal of Artificial Intelligence Research, v. 64, p. 645-703, 2019Tradução . . Disponível em: https://doi.org/10.1613/jair.1.11396. Acesso em: 04 fev. 2023.
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      Silva, F. L. da, & Reali Costa, A. H. (2019). A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems. Journal of Artificial Intelligence Research, 64, 645-703. doi:10.1613/jair.1.11396
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      Silva FL da, Reali Costa AH. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems [Internet]. Journal of Artificial Intelligence Research. 2019 ; 64 645-703.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1613/jair.1.11396
    • Vancouver

      Silva FL da, Reali Costa AH. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems [Internet]. Journal of Artificial Intelligence Research. 2019 ; 64 645-703.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1613/jair.1.11396
  • Source: Computers & Electrical Engineering. Unidade: EP

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      BAJWA, Imran Sarwar e REALI COSTA, Anna Helena e BENTAHAR, Jamal. Introduction to the Special Section on Artificial Intelligence for Smart Systems. Computers & Electrical Engineering, v. 79, p. 1-3, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.compeleceng.2019.106469. Acesso em: 04 fev. 2023.
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      Bajwa, I. S., Reali Costa, A. H., & Bentahar, J. (2019). Introduction to the Special Section on Artificial Intelligence for Smart Systems. Computers & Electrical Engineering, 79, 1-3. doi:10.1016/j.compeleceng.2019.106469
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      Bajwa IS, Reali Costa AH, Bentahar J. Introduction to the Special Section on Artificial Intelligence for Smart Systems [Internet]. Computers & Electrical Engineering. 2019 ; 79 1-3.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.compeleceng.2019.106469
    • Vancouver

      Bajwa IS, Reali Costa AH, Bentahar J. Introduction to the Special Section on Artificial Intelligence for Smart Systems [Internet]. Computers & Electrical Engineering. 2019 ; 79 1-3.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.compeleceng.2019.106469
  • Source: Image and Vision Computing. Unidade: EP

    Subjects: VISÃO COMPUTACIONAL, IMAGEM 3D

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      PERAFAN VILLOTA, Juan Carlos et al. Pairwise registration in indoor environments using adaptive combination of 2D and 3D cues. Image and Vision Computing, v. 69, p. 113-124, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.imavis.2017.08.008. Acesso em: 04 fev. 2023.
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      Perafan Villota, J. C., Reali Costa, A. H., Silva, F. L. da, & Jacomini, R. de S. (2018). Pairwise registration in indoor environments using adaptive combination of 2D and 3D cues. Image and Vision Computing, 69, 113-124. doi:10.1016/j.imavis.2017.08.008
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      Perafan Villota JC, Reali Costa AH, Silva FL da, Jacomini R de S. Pairwise registration in indoor environments using adaptive combination of 2D and 3D cues [Internet]. Image and Vision Computing. 2018 ; 69 113-124.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.imavis.2017.08.008
    • Vancouver

      Perafan Villota JC, Reali Costa AH, Silva FL da, Jacomini R de S. Pairwise registration in indoor environments using adaptive combination of 2D and 3D cues [Internet]. Image and Vision Computing. 2018 ; 69 113-124.[citado 2023 fev. 04 ] Available from: https://doi.org/10.1016/j.imavis.2017.08.008
  • Source: Journal of Computational Biology. Unidade: EP

    Assunto: FRAMEWORKS

    Acesso à fonteDOIHow to cite
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      JACOMINI, Ricardo de Souza et al. GeNICE: a Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis. Journal of Computational Biology, 2017Tradução . . Disponível em: https://doi.org/10.1089/cmb.2017.0022. Acesso em: 04 fev. 2023.
    • APA

      Jacomini, R. de S., Martins-Jr, D., Silva, F. L. da, & Reali Costa, A. H. (2017). GeNICE: a Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis. Journal of Computational Biology. doi:10.1089/cmb.2017.0022
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      Jacomini R de S, Martins-Jr D, Silva FL da, Reali Costa AH. GeNICE: a Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis [Internet]. Journal of Computational Biology. 2017 ;[citado 2023 fev. 04 ] Available from: https://doi.org/10.1089/cmb.2017.0022
    • Vancouver

      Jacomini R de S, Martins-Jr D, Silva FL da, Reali Costa AH. GeNICE: a Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis [Internet]. Journal of Computational Biology. 2017 ;[citado 2023 fev. 04 ] Available from: https://doi.org/10.1089/cmb.2017.0022
  • Source: Computers and Electronics in Agriculture. Unidades: EACH, EP, FMRP

    Subjects: ABELHAS (CLASSIFICAÇÃO;IDENTIFICAÇÃO), MORFOMETRIA

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      SILVA, Felipe Leno da et al. Evaluating classification and feature selection techniques for honeybee subspecies identification using wing images. Computers and Electronics in Agriculture, v. 114, n. ju 2015, p. 68-77, 2015Tradução . . Disponível em: http://dx.doi.org/10.1016/j.compag.2015.03.012. Acesso em: 04 fev. 2023.
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      Silva, F. L. da, Grassi-Sella, M. L., Francoy, T. M., & Reali Costa, A. H. (2015). Evaluating classification and feature selection techniques for honeybee subspecies identification using wing images. Computers and Electronics in Agriculture, 114( ju 2015), 68-77. doi:10.1016/j.compag.2015.03.012
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      Silva FL da, Grassi-Sella ML, Francoy TM, Reali Costa AH. Evaluating classification and feature selection techniques for honeybee subspecies identification using wing images [Internet]. Computers and Electronics in Agriculture. 2015 ; 114( ju 2015): 68-77.[citado 2023 fev. 04 ] Available from: http://dx.doi.org/10.1016/j.compag.2015.03.012
    • Vancouver

      Silva FL da, Grassi-Sella ML, Francoy TM, Reali Costa AH. Evaluating classification and feature selection techniques for honeybee subspecies identification using wing images [Internet]. Computers and Electronics in Agriculture. 2015 ; 114( ju 2015): 68-77.[citado 2023 fev. 04 ] Available from: http://dx.doi.org/10.1016/j.compag.2015.03.012
  • Source: IEEE Transactions on Cybernetics. Unidades: EACH, EP

    Subjects: DINÂMICA ESTOCÁSTICA, ANÁLISE ESTOCÁSTICA, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL

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      KOGA, Marcelo Li e SILVA, Valdinei Freire da e REALI COSTA, Anna Helena. Stochastic abstract policies: generalizing knowledge to improve reinforcement learning. IEEE Transactions on Cybernetics, v. 45, n. ja 2015, p. 77-88, 2015Tradução . . Disponível em: http://dx.doi.org/10.1109/TCYB.2014.2319733. Acesso em: 04 fev. 2023.
    • APA

      Koga, M. L., Silva, V. F. da, & Reali Costa, A. H. (2015). Stochastic abstract policies: generalizing knowledge to improve reinforcement learning. IEEE Transactions on Cybernetics, 45( ja 2015), 77-88. doi:10.1109/TCYB.2014.2319733
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      Koga ML, Silva VF da, Reali Costa AH. Stochastic abstract policies: generalizing knowledge to improve reinforcement learning [Internet]. IEEE Transactions on Cybernetics. 2015 ; 45( ja 2015): 77-88.[citado 2023 fev. 04 ] Available from: http://dx.doi.org/10.1109/TCYB.2014.2319733
    • Vancouver

      Koga ML, Silva VF da, Reali Costa AH. Stochastic abstract policies: generalizing knowledge to improve reinforcement learning [Internet]. IEEE Transactions on Cybernetics. 2015 ; 45( ja 2015): 77-88.[citado 2023 fev. 04 ] Available from: http://dx.doi.org/10.1109/TCYB.2014.2319733

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