Filtros : "Barr, Brian" Limpar

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  • Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, VISUALIZAÇÃO, MODELOS PARA PROCESSOS ESTOCÁSTICOS

    PrivadoAcesso à fonteDOIHow to cite
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    • ABNT

      XENOPOULOS, Peter et al. Calibrate: interactive analysis of probabilistic model output. IEEE Transactions on Visualization and Computer Graphics, v. 29, n. Ja 2023, p. 853-863, 2023Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2022.3209489. Acesso em: 07 jun. 2024.
    • APA

      Xenopoulos, P., Rulff, J., Nonato, L. G., Barr, B., & Silva, C. (2023). Calibrate: interactive analysis of probabilistic model output. IEEE Transactions on Visualization and Computer Graphics, 29( Ja 2023), 853-863. doi:10.1109/TVCG.2022.3209489
    • NLM

      Xenopoulos P, Rulff J, Nonato LG, Barr B, Silva C. Calibrate: interactive analysis of probabilistic model output [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( Ja 2023): 853-863.[citado 2024 jun. 07 ] Available from: https://doi.org/10.1109/TVCG.2022.3209489
    • Vancouver

      Xenopoulos P, Rulff J, Nonato LG, Barr B, Silva C. Calibrate: interactive analysis of probabilistic model output [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( Ja 2023): 853-863.[citado 2024 jun. 07 ] Available from: https://doi.org/10.1109/TVCG.2022.3209489
  • Source: IEEE Computer Graphics and Applications. Unidade: ICMC

    Subjects: CLUSTERS, VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL

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    • ABNT

      YUAN, Jun et al. SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level. IEEE Computer Graphics and Applications, v. 42, n. 6, p. 24-36, 2022Tradução . . Disponível em: https://doi.org/10.1109/MCG.2022.3199727. Acesso em: 07 jun. 2024.
    • APA

      Yuan, J., Chan, G. Y. -Y., Barr, B., Overton, K., Rees, K., Nonato, L. G., et al. (2022). SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level. IEEE Computer Graphics and Applications, 42( 6), 24-36. doi:10.1109/MCG.2022.3199727
    • NLM

      Yuan J, Chan GY-Y, Barr B, Overton K, Rees K, Nonato LG, Bertini E, Silva CT. SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level [Internet]. IEEE Computer Graphics and Applications. 2022 ; 42( 6): 24-36.[citado 2024 jun. 07 ] Available from: https://doi.org/10.1109/MCG.2022.3199727
    • Vancouver

      Yuan J, Chan GY-Y, Barr B, Overton K, Rees K, Nonato LG, Bertini E, Silva CT. SUBPLEX: a Visual analytics approach to understand local model explanations at the subpopulation level [Internet]. IEEE Computer Graphics and Applications. 2022 ; 42( 6): 24-36.[citado 2024 jun. 07 ] Available from: https://doi.org/10.1109/MCG.2022.3199727
  • Source: Proceedings of Machine Learning Research - PMLR. Conference titles: Workshop on Topology, Algebra, and Geometry in Machine Learning. Unidade: ICMC

    Subjects: TOPOLOGIA, PREDIÇÃO, FRAMEWORKS

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    • ABNT

      XENOPOULOS, Peter et al. GALE: globally assessing local explanations. Proceedings of Machine Learning Research - PMLR. Brookline: Microtome Publishing. Disponível em: https://proceedings.mlr.press/v196/xenopoulos22a.html. Acesso em: 07 jun. 2024. , 2022
    • APA

      Xenopoulos, P., Silva, C., Chan, G. Y. -Y., Doraiswamy, H., Nonato, L. G., Barr, B., & Silva, C. (2022). GALE: globally assessing local explanations. Proceedings of Machine Learning Research - PMLR. Brookline: Microtome Publishing. Recuperado de https://proceedings.mlr.press/v196/xenopoulos22a.html
    • NLM

      Xenopoulos P, Silva C, Chan GY-Y, Doraiswamy H, Nonato LG, Barr B, Silva C. GALE: globally assessing local explanations [Internet]. Proceedings of Machine Learning Research - PMLR. 2022 ; 196 322-331.[citado 2024 jun. 07 ] Available from: https://proceedings.mlr.press/v196/xenopoulos22a.html
    • Vancouver

      Xenopoulos P, Silva C, Chan GY-Y, Doraiswamy H, Nonato LG, Barr B, Silva C. GALE: globally assessing local explanations [Internet]. Proceedings of Machine Learning Research - PMLR. 2022 ; 196 322-331.[citado 2024 jun. 07 ] Available from: https://proceedings.mlr.press/v196/xenopoulos22a.html

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