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 401–425 of 537 papers

TitleStatusHype
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patientsCode0
Full-Gradient Representation for Neural Network VisualizationCode0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsCode0
Understanding Neural Networks Through Deep VisualizationCode0
Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine LearningCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
Supervised Feature Compression based on Counterfactual AnalysisCode0
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of SepsisCode0
Fast Parallel Exact Inference on Bayesian Networks: PosterCode0
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networksCode0
Causality-based Counterfactual Explanation for Classification ModelsCode0
How to See Hidden Patterns in Metamaterials with Interpretable Machine LearningCode0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Gaining Free or Low-Cost Transparency with Interpretable Partial SubstituteCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic RegressionCode0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapyCode0
COLOGNE: Coordinated Local Graph Neighborhood SamplingCode0
iNNvestigate neural networks!Code0
Explaining Hyperparameter Optimization via Partial Dependence PlotsCode0
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Benchmark Results

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