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 351–375 of 537 papers

TitleStatusHype
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records—0
Are machine learning interpretations reliable? A stability study on global interpretations—0
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning—0
The Partial Response Network: a neural network nomogram—0
Model Bridging: Connection between Simulation Model and Neural Network—0
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach—0
The Promise and Peril of Human Evaluation for Model Interpretability—0
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
Motif-guided Time Series Counterfactual Explanations—0
Multi-Agent Algorithmic Recourse—0
A Novel Memetic Strategy for Optimized Learning of Classification Trees—0
Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions—0
Multi-type Disentanglement without Adversarial Training—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Near Optimal Decision Trees in a SPLIT Second—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
Neural-ANOVA: Model Decomposition for Interpretable Machine Learning—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods In Psychiatry Detection Applications, Specifically Depression Disorder: A Brief Review.—0
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models—0
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
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Benchmark Results

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