1 citations · 1 across the 2 of their papers we have counts for
3 papers
Positional Encoder Graph Quantile Neural Networks for Geographic Data
William E. R. de Amorim, Scott A. Sisson, T. Rodrigues +2
Positional Encoder Graph Neural Networks (PE-GNNs) are among the most effective models for learning from continuous spatial data. However, their predictive distributions are often…
Text clustering applied to data augmentation in legal contexts
Lucas José Gonçalves Freitas, Thaís Rodrigues, Guilherme Rodrigues +2
Data analysis and machine learning are of preeminent importance in the legal domain, especially in tasks like clustering and text classification. In this study, we harnessed the po…
Model-Free Local Recalibration of Neural Networks
R. Torres, D. J. Nott, S. A. Sisson +3
Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been m…