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 76–100 of 537 papers

TitleStatusHype
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