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 101–125 of 537 papers

TitleStatusHype
On the definition and importance of interpretability in scientific machine learning—0
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques—0
Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning—0
Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian GeometryCode0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Attention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models—0
Towards Probabilistic Dynamic Security Assessment and Enhancement of Large Power Systems—0
NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy ForecastingCode0
Interpretable machine learning-guided design of Fe-based soft magnetic alloys—0
Causal rule ensemble approach for multi-arm data—0
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution AnalysisCode0
Towards Simple Machine Learning Baselines for GNSS RFI Detection—0
Interpretable Machine Learning in Physics: A Review—0
Kernel Learning Assisted Synthesis Condition Exploration for Ternary SpinelCode0
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change—0
Predicting and Understanding College Student Mental Health with Interpretable Machine LearningCode0
Diagnostic-free onboard battery health assessment—0
A Frank System for Co-Evolutionary Hybrid Decision-Making—0
Near Optimal Decision Trees in a SPLIT Second—0
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning—0
Interpretable Machine Learning for Kronecker Coefficients—0
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine LearningCode0
High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture—0
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Benchmark Results

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