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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 537 papers

TitleStatusHype
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning—0
META-ANOVA: Screening interactions for interpretable machine learning—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
A Survey of Malware Detection Using Deep Learning—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model—0
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning PredictionsCode0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Machine Learning for Economic Forecasting: An Application to China's GDP Growth—0
Selecting Interpretability Techniques for Healthcare Machine Learning models—0
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector—0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Tensor Polynomial Additive Model—0
Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*Code0
Learning Discrete Concepts in Latent Hierarchical Models—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Predicting Many Crystal Properties via an Adaptive Transformer-based Framework—0
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear RegressionCode0
Review of Interpretable Machine Learning Models for Disease Prognosis—0
Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesCode0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
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Benchmark Results

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