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 51–100 of 537 papers

TitleStatusHype
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualizationCode1
Trees with Attention for Set Prediction TasksCode1
Do Feature Attribution Methods Correctly Attribute Features?Code1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Modern Hopfield Networks and Attention for Immune Repertoire ClassificationCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Axiomatic Attribution for Deep NetworksCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
GAM Changer: Editing Generalized Additive Models with Interactive VisualizationCode1
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable LearningCode1
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window TransformerCode1
Born-Again Tree EnsemblesCode1
Graph Learning for Numeric PlanningCode1
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
Fast Sparse Decision Tree Optimization via Reference EnsemblesCode1
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
Interpretable Machine Learning with an Ensemble of Gradient Boosting MachinesCode1
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
Improving performance of deep learning models with axiomatic attribution priors and expected gradientsCode1
Interpretable machine learning for high-dimensional trajectories of aging healthCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Attention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models—0
Analysis and classification of main risk factors causing stroke in Shanxi Province—0
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
A Survey of Malware Detection Using Deep Learning—0
Linguistically inspired roadmap for building biologically reliable protein language models—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
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Benchmark Results

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