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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 251–260 of 537 papers

TitleStatusHype
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons—0
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
Insights into the origin of halo mass profiles from machine learning—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Attention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models—0
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles—0
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Benchmark Results

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