A Bayesian framework produces relevance attribution distributions for power quality disturbance classifiers so experts can select explanations by confidence percentiles.
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DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.
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A Bayesian Framework for Uncertainty-Aware Explanations in Power Quality Disturbance Classification
A Bayesian framework produces relevance attribution distributions for power quality disturbance classifiers so experts can select explanations by confidence percentiles.
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DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.