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 451–460 of 537 papers

TitleStatusHype
Exploring Interpretability for Predictive Process Analytics—0
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
A Decision-Theoretic Approach for Model Interpretability in Bayesian FrameworkCode0
Topological data analysis of zebrafish patterns—0
Understanding Deep Networks via Extremal Perturbations and Smooth MasksCode1
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of SepsisCode0
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Benchmark Results

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