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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 501–537 of 537 papers

TitleStatusHype
RISE: Randomized Input Sampling for Explanation of Black-box ModelsCode1
Contrastive Explanations with Local Foil TreesCode0
Hierarchical interpretations for neural network predictionsCode1
Learning Kolmogorov Models for Binary Random Variables—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
Brain Age from the Electroencephalogram of Sleep—0
Probing hidden spin order with interpretable machine learningCode0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
Manipulating and Measuring Model InterpretabilityCode0
Gaining Free or Low-Cost Transparency with Interpretable Partial SubstituteCode0
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine LearningCode0
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning—0
The Promise and Peril of Human Evaluation for Model Interpretability—0
The Doctor Just Won't Accept That!—0
The (Un)reliability of saliency methodsCode0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
SmoothGrad: removing noise by adding noiseCode4
A Unified Approach to Interpreting Model PredictionsCode1
Interpretable Explanations of Black Boxes by Meaningful PerturbationCode0
Learning Important Features Through Propagating Activation DifferencesCode4
Axiomatic Attribution for Deep NetworksCode1
Towards A Rigorous Science of Interpretable Machine Learning—0
"What is Relevant in a Text Document?": An Interpretable Machine Learning ApproachCode0
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems—0
GENESIM: genetic extraction of a single, interpretable modelCode0
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance—0
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability—0
Interpretable Machine Learning Models for the Digital Clock Drawing Test—0
Interpretable Two-level Boolean Rule Learning for Classification—0
"Why Should I Trust You?": Explaining the Predictions of Any ClassifierCode1
Understanding Neural Networks Through Deep VisualizationCode0
Supersparse Linear Integer Models for Optimized Medical Scoring SystemsCode0
Predictive learning via rule ensembles—0
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Benchmark Results

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