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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 301–310 of 537 papers

TitleStatusHype
Linguistically inspired roadmap for building biologically reliable protein language models—0
Knowledge Discovery from Atomic Structures using Feature Importances—0
Knowledge Representation with Conceptual Spaces—0
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems—0
LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Models—0
Learning Discrete Concepts in Latent Hierarchical Models—0
Attention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models—0
Structural Node Embeddings with Homomorphism Counts—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
Learning Kolmogorov Models for Binary Random Variables—0
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Benchmark Results

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