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 1–50 of 537 papers

TitleStatusHype
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
SmoothGrad: removing noise by adding noiseCode4
Learning Important Features Through Propagating Activation DifferencesCode4
PiML Toolbox for Interpretable Machine Learning Model Development and DiagnosticsCode3
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlCode2
OmniXAI: A Library for Explainable AICode2
Designing Inherently Interpretable Machine Learning ModelsCode2
Neurosymbolic Association Rule Mining from Tabular DataCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Graph Learning for Numeric PlanningCode1
LLM-SR: Scientific Equation Discovery via Programming with Large Language ModelsCode1
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Interpretable Machine Learning for TabPFNCode1
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable LearningCode1
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window TransformerCode1
Learning Transformer ProgramsCode1
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
Take 5: Interpretable Image Classification with a Handful of FeaturesCode1
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitisCode1
Structural Neural Additive Models: Enhanced Interpretable Machine LearningCode1
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
Mixture of Decision Trees for Interpretable Machine LearningCode1
TSInterpret: A unified framework for time series interpretabilityCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Neural Basis Models for InterpretabilityCode1
Scalable Interpretability via PolynomialsCode1
Towards Better Understanding Attribution MethodsCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
POTATO: exPlainable infOrmation exTrAcTion framewOrkCode1
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Fast Sparse Decision Tree Optimization via Reference EnsemblesCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Shapley variable importance clouds for interpretable machine learningCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Trees with Attention for Set Prediction TasksCode1
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Benchmark Results

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