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Found 3 papers, 1 papers with code
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components.
Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation
This paper introduces a novel technique called counterfactual knowledge distillation (CFKD) to detect and remove reliance on confounders in deep learning models with the help of human expert feedback.
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Explanations often take the form of a heatmap identifying input features (e. g. pixels) that are relevant to the model's decision.
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