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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 537 papers

TitleStatusHype
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Interpretable Machine Learning: Moving From Mythos to Diagnostics—0
CoDeGAN: Contrastive Disentanglement for Generative Adversarial NetworkCode0
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsCode0
On Interpretability and Similarity in Concept-Based Machine Learning—0
Consistent Sparse Deep Learning: Theory and ComputationCode0
Interpretable Predictive Maintenance for Hard Drives—0
COLOGNE: Coordinated Local Graph Neighborhood SamplingCode0
[Re] Explaining Groups of Points in Low-Dimensional RepresentationsCode0
[Re] Explaining Groups of Points in Low-Dimensional RepresentationsCode0
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs—0
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
Challenging common interpretability assumptions in feature attribution explanationsCode0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Quantifying and Learning Linear Symmetry-Based DisentanglementCode0
A Learning Theoretic Perspective on Local Explainability—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
On Explaining Decision Trees—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
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Benchmark Results

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