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

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Showing 51–60 of 537 papers

TitleStatusHype
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Interpretable Machine Learning for TabPFNCode1
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
Learning Transformer ProgramsCode1
LLM-SR: Scientific Equation Discovery via Programming with Large Language ModelsCode1
A Unified Approach to Interpreting Model PredictionsCode1
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution MethodsCode1
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Benchmark Results

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