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 381–390 of 537 papers

TitleStatusHype
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations—0
System Design for a Data-driven and Explainable Customer Sentiment MonitorCode0
Extract Local Inference Chains of Deep Neural Nets—0
Multi-type Disentanglement without Adversarial Training—0
PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learningCode0
Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data—0
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Challenging common interpretability assumptions in feature attribution explanationsCode0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Neural Prototype Trees for Interpretable Fine-grained Image RecognitionCode1
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Benchmark Results

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