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Found 3 papers, 1 papers with code
Do not explain without context: addressing the blind spot of model explanations
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising greater interpretability.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Deep spatial context: when attention-based models meet spatial regression
The DSCon allows for a quantitative measure of the spatial context's role using three Spatial Context Measures: $SCM_{features}$, $SCM_{targets}$, $SCM_{residuals}$ to distinguish whether the spatial context is observable within the features of neighboring regions, their target values (attention scores) or residuals, respectively.
Hybrid Ensemble-Based Travel Mode Prediction
To address the challenge of the development of TMC models, we propose the novel Incremental Ensemble of Batch and Stream Models (IEBSM) method aimed at adapting travel mode choice classifiers to concept drift possibly occurring in the data.
