Time series causal relationships discovery through feature importance and ensemble models

Autor: Manuel Castro, Pedro Ribeiro Mendes Júnior, Aurea Soriano-Vargas, Rafael de Oliveira Werneck, Maiara Moreira Gonçalves, Leopoldo Lusquino Filho, Renato Moura, Marcelo Zampieri, Oscar Linares, Vitor Ferreira, Alexandre Ferreira, Alessandra Davólio, Denis Schiozer, Anderson Rocha

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-18 (2023)

Abstract Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algo

Externí odkaz: https://doaj.org/article/b28347ad19fb496e8d9465157c4e12e8

Oil production and pressure multimodal forecasting integrating high-frequency production data

Autor: Alexandre Mello Ferreira, Leopoldo André Dutra Lusquino Filho, Anderson de Rezende Rocha, Pedro Ribeiro Mendes Júnior, Manuel Castro, Renato Moura, Alessandra Davólio Gomes, Vitor Hugo de Sousa Ferreira, Rafael de Oliveira Werneck, Eduardo dos Santos Pereira Eduardo Pereira

Publikováno v: Rio Oil and Gas Expo and Conference. 22:308-309

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::708a13bd65079a486367f22588b975d8
https://doi.org/10.48072/2525-7579.rog.2022.308

A unified framework for design, deployment, execution, and recommendation of machine learning experiments

Autor: Rafael de Oliveira Werneck

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a1849a811d6b0124627095d0721fc622
https://doi.org/10.47749/t/unicamp.2014.935835

Kuaa: A unified framework for design, deployment, execution, and recommendation of machine learning experiments

Autor: Pedro Ribeiro Mendes Júnior, Otavio A. B. Penatti, Anderson Rocha, Daniel Vatanabe Pazinato, Waldir Rodrigues De Almeida, Bernardo Vecchia Stein, Rafael de Oliveira Werneck, Ricardo da Silva Torres

Publikováno v: Future Generation Computer Systems. 78:59-76

In this work, we propose Kuaa, a workflow-based framework that can be used for designing, deploying, and executing machine learning experiments in an automated fashion. This framework is able to provide a standardized environment for exploratory anal

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::38470db85d595e1680cd17826704f6b6
https://doi.org/10.1016/j.future.2017.06.013

Learning cost function for graph classification with open-set methods

Autor: Romain Raveaux, Rafael de Oliveira Werneck, Ricardo da Silva Torres, Salvatore Tabbone

Publikováno v: Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2019, 128, pp.8-15. ⟨10.1016/j.patrec.2019.08.010⟩
Pattern Recognition Letters, 2019, 128, pp.8-15. ⟨10.1016/j.patrec.2019.08.010⟩

In several pattern recognition problems, effective graph matching is of paramount importance. In this paper, we introduce a novel framework to learn discriminative cost functions. These cost functions are embedded into a graph matching-based classifi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e511bb13e03f423da0e58862377dca1
https://hal.archives-ouvertes.fr/hal-02270763