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 61–70 of 537 papers

TitleStatusHype
Optimal Counterfactual Explanations in Tree EnsemblesCode1
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
Axiomatic Attribution for Deep NetworksCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
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Benchmark Results

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