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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 201–225 of 537 papers

TitleStatusHype
Explanations for Automatic Speech Recognition—0
Structural Neural Additive Models: Enhanced Interpretable Machine LearningCode1
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
Data-driven Approach for Static Hedging of Exchange Traded Options—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patientsCode0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—0
Shapley variable importance cloud for machine learning models—0
MAntRA: A framework for model agnostic reliability analysis—0
Fast Parallel Exact Inference on Bayesian Networks: PosterCode0
Interpretability with full complexity by constraining feature information—0
Mixture of Decision Trees for Interpretable Machine LearningCode1
Overcoming Catastrophic Forgetting by XAI—0
A Generic Approach for Reproducible Model DistillationCode0
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
Supervised Feature Compression based on Counterfactual AnalysisCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning—0
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning—0
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Benchmark Results

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