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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 376–400 of 537 papers

TitleStatusHype
Full interpretable machine learning in 2D with inline coordinates—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray ImagesCode0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
An exact counterfactual-example-based approach to tree-ensemble models interpretabilityCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Analysis and classification of main risk factors causing stroke in Shanxi Province—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Comparing interpretability and explainability for feature selection—0
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning—0
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled ExperimentsCode0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Causality-based Counterfactual Explanation for Classification ModelsCode0
Online Product Feature Recommendations with Interpretable Machine Learning—0
From Human Explanation to Model Interpretability: A Framework Based on Weight of EvidenceCode0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer InformationCode0
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structureCode0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
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Benchmark Results

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