1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2022
Characterizing instance hardness in classification and regression problems
Gustavo P. Torquette, Victor S. Nunes, Pedro Y. A. Paiva +2
Some recent pieces of work in the Machine Learning (ML) literature have demonstrated the usefulness of assessing which observations are hardest to have their label predicted accura…
cs.LG2021★ 1 cited
PyHard: a novel tool for generating hardness embeddings to support data-centric analysis
Pedro Yuri Arbs Paiva, Kate Smith-Miles, Maria Gabriela Valeriano +1
For building successful Machine Learning (ML) systems, it is imperative to have high quality data and well tuned learning models. But how can one assess the quality of a given data…