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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 51–60 of 537 papers

TitleStatusHype
Improving performance of deep learning models with axiomatic attribution priors and expected gradientsCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
Mixture of Decision Trees for Interpretable Machine LearningCode1
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
A Unified Approach to Interpreting Model PredictionsCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
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Benchmark Results

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