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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

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Showing 1–25 of 537 papers

TitleStatusHype
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis—0
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
Leveraging Predictive Equivalence in Decision TreesCode0
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images—0
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia—0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study—0
Are machine learning interpretations reliable? A stability study on global interpretations—0
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
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
Neurosymbolic Association Rule Mining from Tabular DataCode1
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Benchmark Results

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