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Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

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Showing 1–10 of 537 papers

TitleStatusHype
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
SmoothGrad: removing noise by adding noiseCode4
Learning Important Features Through Propagating Activation DifferencesCode4
PiML Toolbox for Interpretable Machine Learning Model Development and DiagnosticsCode3
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlCode2
OmniXAI: A Library for Explainable AICode2
Designing Inherently Interpretable Machine Learning ModelsCode2
Neurosymbolic Association Rule Mining from Tabular DataCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified