XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.
Visualizing the Impact of Feature Attribution Baselines
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Counterfactual baselines for Integrated Gradients yield more faithful and medically relevant attributions than standard baselines across three medical datasets.
Path-sampled integrated gradients generalizes integrated gradients by averaging gradients over sampled baselines on the linear path, proving equivalence to a weighted version that improves convergence rate to O(m^{-1}) and reduces variance by a factor of 1/3 under uniform sampling.
citing papers explorer
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From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks
XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.
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On the notion of missingness for path attribution explainability methods in medical settings: Guiding the selection of medically meaningful baselines
Counterfactual baselines for Integrated Gradients yield more faithful and medically relevant attributions than standard baselines across three medical datasets.
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Path-Sampled Integrated Gradients
Path-sampled integrated gradients generalizes integrated gradients by averaging gradients over sampled baselines on the linear path, proving equivalence to a weighted version that improves convergence rate to O(m^{-1}) and reduces variance by a factor of 1/3 under uniform sampling.