SOTAVerified

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 271–280 of 537 papers

TitleStatusHype
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patientsCode0
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Shapley variable importance cloud for machine learning models—0
MAntRA: A framework for model agnostic reliability analysis—0
Fast Parallel Exact Inference on Bayesian Networks: PosterCode0
Interpretability with full complexity by constraining feature information—0
Overcoming Catastrophic Forgetting by XAI—0
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Benchmark Results

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