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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 537 papers

TitleStatusHype
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
Causal rule ensemble approach for multi-arm data—0
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data—0
Generally-Occurring Model Change for Robust Counterfactual Explanations—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models—0
GAM(L)A: An econometric model for interpretable Machine Learning—0
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
Causal Dependence Plots—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization—0
Causal Entropy and Information Gain for Measuring Causal Control—0
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach—0
Causality Learning: A New Perspective for Interpretable Machine Learning—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
Hidden Citations Obscure True Impact in Science—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
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Benchmark Results

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