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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 537 papers

TitleStatusHype
Improving performance of deep learning models with axiomatic attribution priors and expected gradientsCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc InterpretabilityCode1
Interpretable machine learning: definitions, methods, and applicationsCode1
RISE: Randomized Input Sampling for Explanation of Black-box ModelsCode1
Hierarchical interpretations for neural network predictionsCode1
A Unified Approach to Interpreting Model PredictionsCode1
Axiomatic Attribution for Deep NetworksCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
"Why Should I Trust You?": Explaining the Predictions of Any ClassifierCode1
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
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Benchmark Results

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