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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 301–325 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
Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models—0
Learning Model Agnostic Explanations via Constraint Programming—0
Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator—0
A Survey of Malware Detection Using Deep Learning—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Subgroup Analysis via Model-based Rule Forest—0
A Semiparametric Approach to Interpretable Machine Learning—0
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning—0
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Using an interpretable Machine Learning approach to study the drivers of International Migration—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
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Benchmark Results

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