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 376–400 of 537 papers

TitleStatusHype
COLOGNE: Coordinated Local Graph Neighborhood SamplingCode0
[Re] Explaining Groups of Points in Low-Dimensional RepresentationsCode0
[Re] Explaining Groups of Points in Low-Dimensional RepresentationsCode0
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualizationCode1
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
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
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
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
Altruist: Argumentative Explanations through Local Interpretations of Predictive ModelsCode0
Interpretable Machine Learning with an Ensemble of Gradient Boosting MachinesCode1
Novel Topological Shapes of Model Interpretability—0
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Benchmark Results

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