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 176–200 of 537 papers

TitleStatusHype
iNNvestigate neural networks!Code0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Gaining Free or Low-Cost Transparency with Interpretable Partial SubstituteCode0
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networksCode0
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine LearningCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesCode0
Supervised Feature Compression based on Counterfactual AnalysisCode0
MLIC: A MaxSAT-Based framework for learning interpretable classification rulesCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Individualized Prediction of COVID-19 Adverse outcomes with MLHOCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsCode0
Harnessing Interpretable Machine Learning for Holistic Inverse Design of OrigamiCode0
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?Code0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapyCode0
Comparative Document Summarisation via ClassificationCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
An interpretable clustering approach to safety climate analysis: examining driver group distinction in safety climate perceptionsCode0
Dynamic Model Tree for Interpretable Data Stream LearningCode0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
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Benchmark Results

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