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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 451–475 of 537 papers

TitleStatusHype
Explainable Representation Learning of Small Quantum StatesCode0
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing FlowsCode0
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear RegressionCode0
NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy ForecastingCode0
Relative Feature ImportanceCode0
Altruist: Argumentative Explanations through Local Interpretations of Predictive ModelsCode0
Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*Code0
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Offensive Language Detection ExplainedCode0
Interpretable Machine Learning for Survival AnalysisCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network RobustnessCode0
Big Earth Data and Machine Learning for Sustainable and Resilient AgricultureCode0
REPID: Regional Effect Plots with implicit Interaction DetectionCode0
Re-Ranking Words to Improve Interpretability of Automatically Generated TopicsCode0
Visualization of Convolutional Neural Networks for Monocular Depth EstimationCode0
Online Learning of Decision Trees with Thompson SamplingCode0
"What is Relevant in a Text Document?": An Interpretable Machine Learning ApproachCode0
Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin LiquidsCode0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution AnalysisCode0
Worth of knowledge in deep learningCode0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous DatasetsCode0
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
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Benchmark Results

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