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 226–250 of 537 papers

TitleStatusHype
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
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
Explainable AI using expressive Boolean formulas—0
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
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Benchmark Results

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