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 351–400 of 537 papers

TitleStatusHype
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Comparing interpretability and explainability for feature selection—0
Interpretable machine learning for high-dimensional trajectories of aging healthCode1
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning—0
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled ExperimentsCode0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Causality-based Counterfactual Explanation for Classification ModelsCode0
Online Product Feature Recommendations with Interpretable Machine Learning—0
Do Feature Attribution Methods Correctly Attribute Features?Code1
From Human Explanation to Model Interpretability: A Framework Based on Weight of EvidenceCode0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
Grouped Feature Importance and Combined Features Effect PlotCode1
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer InformationCode0
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structureCode0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
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
Consistent Sparse Deep Learning: Theory and ComputationCode0
On Interpretability and Similarity in Concept-Based Machine Learning—0
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
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
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