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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 81–90 of 537 papers

Title	Date	Tasks	Status	Hype	Score
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data	May 5, 2022	BIG-bench Machine LearningFeature Importance	CodeCode Available	1	5
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees	May 18, 2019	Feature EngineeringFeature Importance	CodeCode Available	1	5
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution	Jun 11, 2022	Bayesian OptimizationHyperparameter Optimization	CodeCode Available	1	5
Modern Hopfield Networks and Attention for Immune Repertoire Classification	Jul 16, 2020	BIG-bench Machine LearningClassification	CodeCode Available	1	5
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification	Dec 9, 2020	Anomaly DetectionAtrial Fibrillation Detection	CodeCode Available	1	5
GFN-SR: Symbolic Regression with Generative Flow Networks	Dec 1, 2023	Deep Reinforcement LearningInterpretable Machine Learning	CodeCode Available	0	5
GENESIM: genetic extraction of a single, interpretable model	Nov 17, 2016	Decision MakingInterpretable Machine Learning	CodeCode Available	0	5
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition	Jun 10, 2025	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available	0	5
ProtoAttend: Attention-Based Prototypical Learning	Feb 17, 2019	Decision MakingGeneral Classification	CodeCode Available	0	5
An Additive Instance-Wise Approach to Multi-class Model Interpretation	Jul 7, 2022	Additive modelsInterpretable Machine Learning	CodeCode Available	0	5

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Q-SENN	Top 1 Accuracy	85.9	—	Unverified
2	SLDD-Model	Top 1 Accuracy	85.7	—	Unverified