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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
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
SmoothGrad: removing noise by adding noiseCode4
Learning Important Features Through Propagating Activation DifferencesCode4
PiML Toolbox for Interpretable Machine Learning Model Development and DiagnosticsCode3
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlCode2
OmniXAI: A Library for Explainable AICode2
Designing Inherently Interpretable Machine Learning ModelsCode2
Neurosymbolic Association Rule Mining from Tabular DataCode1
Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and InterpretationCode1
Graph Learning for Numeric PlanningCode1
LLM-SR: Scientific Equation Discovery via Programming with Large Language ModelsCode1
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Interpretable Machine Learning for TabPFNCode1
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable LearningCode1
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window TransformerCode1
Learning Transformer ProgramsCode1
ExeKGLib: Knowledge Graphs-Empowered Machine Learning AnalyticsCode1
Take 5: Interpretable Image Classification with a Handful of FeaturesCode1
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Benchmark Results

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