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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 461–470 of 537 papers

TitleStatusHype
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
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Benchmark Results

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