SOTAVerified

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 51–75 of 537 papers

TitleStatusHype
Optimal Counterfactual Explanations in Tree EnsemblesCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Interpretable machine learning for high-dimensional trajectories of aging healthCode1
Do Feature Attribution Methods Correctly Attribute Features?Code1
Grouped Feature Importance and Combined Features Effect PlotCode1
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualizationCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Neural Prototype Trees for Interpretable Fine-grained Image RecognitionCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Interpretable Machine Learning with an Ensemble of Gradient Boosting MachinesCode1
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction TaskCode1
Interpretable Machine Learning Approaches to Prediction of Chronic HomelessnessCode1
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals PredictionCode1
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit LayersCode1
Modern Hopfield Networks and Attention for Immune Repertoire ClassificationCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
How Interpretable and Trustworthy are GAMs?Code1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
Neural Additive Models: Interpretable Machine Learning with Neural NetsCode1
Understanding the decisions of CNNs: An in-model approachCode1
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
Born-Again Tree EnsemblesCode1
Understanding Deep Networks via Extremal Perturbations and Smooth MasksCode1
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Benchmark Results

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