Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Nonato, Luís Gustavo"'
Autor:
Guardieiro, Vitoria, de Oliveira, Felipe Inagaki, Doraiswamy, Harish, Nonato, Luis Gustavo, Silva, Claudio
High-dimensional data, characterized by many features, can be difficult to visualize effectively. Dimensionality reduction techniques, such as PCA, UMAP, and t-SNE, address this challenge by projecting the data into a lower-dimensional space while pr
Externí odkaz:
http://arxiv.org/abs/2409.07257
Autor:
Solunke, Parikshit, Guardieiro, Vitoria, Rulff, Joao, Xenopoulos, Peter, Chan, Gromit Yeuk-Yin, Barr, Brian, Nonato, Luis Gustavo, Silva, Claudio
With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountability for model predictions. As a result, a large number of local explain
Externí odkaz:
http://arxiv.org/abs/2406.15613
Machine learning and deep learning models are pivotal in educational contexts, particularly in predicting student success. Despite their widespread application, a significant gap persists in comprehending the factors influencing these models' predict
Externí odkaz:
http://arxiv.org/abs/2405.13957
The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and generalize intricate patterns hidden in massive datasets. Modern learning mo
Externí odkaz:
http://arxiv.org/abs/2404.16495
Autor:
Silveira, Jaqueline, García, Germain, Paiva, Afonso, Nery, Marcelo, Adorno, Sergio, Nonato, Luis Gustavo
Extracting relevant urban patterns from multiple data sources can be difficult using classical clustering algorithms since we have to make a suitable setup of the hyperparameters of the algorithms and deal with outliers. It should be addressed correc
Externí odkaz:
http://arxiv.org/abs/2210.02623
Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports b
Externí odkaz:
http://arxiv.org/abs/2207.13770
Publikováno v:
IEEE TVCG 29 (2023) 3105-3120
To reduce the number of pending cases and conflicting rulings in the Brazilian Judiciary, the National Congress amended the Constitution, allowing the Brazilian Supreme Court (STF) to create binding precedents (BPs), i.e., a set of understandings tha
Externí odkaz:
http://arxiv.org/abs/2203.02001
Autor:
Xenopoulos, Peter, Chan, Gromit, Doraiswamy, Harish, Nonato, Luis Gustavo, Barr, Brian, Silva, Claudio
Local explainability methods -- those which seek to generate an explanation for each prediction -- are becoming increasingly prevalent due to the need for practitioners to rationalize their model outputs. However, comparing local explainability metho
Externí odkaz:
http://arxiv.org/abs/2201.02155
Multidimensional Projection is a fundamental tool for high-dimensional data analytics and visualization. With very few exceptions, projection techniques are designed to map data from a high-dimensional space to a visual space so as to preserve some d
Externí odkaz:
http://arxiv.org/abs/2009.01512
Autor:
Yuan, Jun, Chan, Gromit Yeuk-Yin, Barr, Brian, Overton, Kyle, Rees, Kim, Nonato, Luis Gustavo, Bertini, Enrico, Silva, Claudio T.
Understanding the interpretation of machine learning (ML) models has been of paramount importance when making decisions with societal impacts such as transport control, financial activities, and medical diagnosis. While current model interpretation m
Externí odkaz:
http://arxiv.org/abs/2007.10609