41 citations · 85 across the 16 of their papers we have counts for
5 papers · 1 filter
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations
Parikshit Solunke, Vitoria Guardieiro, Joao Rulff +5
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 accountabili…
Exploring the Relationship Between Feature Attribution Methods and Model Performance
Priscylla Silva, Claudio T. Silva, Luis Gustavo Nonato
Machine learning and deep learning models are pivotal in educational contexts, particularly in predicting student success. Despite their widespread application, a significant gap p…
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
Evandro S. Ortigossa, Fábio F. Dias, Brian Barr +2
The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and genera…
TensorAnalyzer: Identification of Urban Patterns in Big Cities using Non-Negative Tensor Factorization
Jaqueline Silveira, Germain García, Afonso Paiva +3
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 o…
Topological Representations of Local Explanations
Peter Xenopoulos, Gromit Chan, Harish Doraiswamy +3
Local explainability methods -- those which seek to generate an explanation for each prediction -- are becoming increasingly prevalent due to the need for practitioners to rational…