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 91–100 of 537 papers

TitleStatusHype
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Attention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models—0
Analysis and classification of main risk factors causing stroke in Shanxi Province—0
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
A Survey of Malware Detection Using Deep Learning—0
Linguistically inspired roadmap for building biologically reliable protein language models—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
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Benchmark Results

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