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 151–200 of 537 papers

TitleStatusHype
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning—0
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions—0
Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs—0
Causal Entropy and Information Gain for Measuring Causal Control—0
Operator-Based Detecting, Learning, and Stabilizing Unstable Periodic Orbits of Chaotic Attractors—0
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments—0
Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity—0
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study—0
Structural Node Embeddings with Homomorphism Counts—0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Is Grad-CAM Explainable in Medical Images?—0
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans—0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
Worth of knowledge in deep learningCode0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
Explainable Representation Learning of Small Quantum StatesCode0
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window TransformerCode1
Explainable AI using expressive Boolean formulas—0
Learning Transformer ProgramsCode1
Loss-Optimal Classification Trees: A Generalized Framework and the Logistic CaseCode0
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization—0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—0
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons—0
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower—0
A Novel Memetic Strategy for Optimized Learning of Classification Trees—0
PiML Toolbox for Interpretable Machine Learning Model Development and DiagnosticsCode3
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlCode2
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?—0
Differentiable Genetic Programming for High-dimensional Symbolic Regression—0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation—0
Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach—0
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing FlowsCode0
Verifying Properties of Tsetlin MachinesCode0
Take 5: Interpretable Image Classification with a Handful of FeaturesCode1
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models—0
Causal Dependence Plots—0
Predicting crash injury severity in smart cities: a novel computational approach with wide and deep learning modelCode0
Knowledge Discovery from Atomic Structures using Feature Importances—0
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitisCode1
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Benchmark Results

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