Interpretable Machine Learning
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Benchmarks
CUB-200-2011
Most implemented
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Axiomatic Attribution for Deep Networks
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
SmoothGrad: removing noise by adding noise
A Unified Approach to Interpreting Model Predictions
Papers
Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was establish…
Interpretable Machine LearningHyperparameter OptimizationSearch Strategies for Optimal Classification and Regression Trees
Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging. While recent work has proposed a variety of search str…
Interpretable Machine LearningNMINE: Normalized Mutual Information Neural Estimation
Mutual information is a general measure of statistical dependence that captures both linear and nonlinear relationships between random variables. For continuous and multidimensional variables For continuous multidimensio…
Interpretable Machine LearningComplexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variabl…
Interpretable Machine LearningA Physics-Regulated Neural Framework for Learning 3D Grain Growth Dynamics
Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws. Developing data-driven surrogates that preserve these physical invariants while remaining computa…
Interpretable Machine LearningInterpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Introduction: Objective neuroimaging biomarkers may improve Parkinson's disease motor assessment by capturing brain variation not directly observable from clinical examination. We used interpretable machine learning to p…
Interpretable Machine Learning