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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 151–160 of 537 papers

TitleStatusHype
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
Gaining Free or Low-Cost Transparency with Interpretable Partial SubstituteCode0
GFN-SR: Symbolic Regression with Generative Flow NetworksCode0
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesCode0
GENESIM: genetic extraction of a single, interpretable modelCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
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Benchmark Results

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