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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 161–170 of 537 papers

TitleStatusHype
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization—0
Causal Entropy and Information Gain for Measuring Causal Control—0
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach—0
Causality Learning: A New Perspective for Interpretable Machine Learning—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Causal rule ensemble approach for multi-arm data—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
Explainable AI using expressive Boolean formulas—0
Show:102550
← PrevPage 17 of 54Next →

Benchmark Results

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