☆ 4.7 Article

Neural architecture search with interpretable meta-features and fast predictors

INFORMATION SCIENCES (2023)

MetaGC-MC: A graph-based meta-learning approach to cold-start recommendation with/without auxiliary information

Honglin Shu et al.

Summary: The MetaGC-MC approach, based on graph convolutional networks and meta-learning, provides effective cold-start recommendations without relying on auxiliary data. By random sampling subgraphs as meta-learning tasks, it captures various subgraph structure information and encodes it as a meta-prior for rapid adaptions. MetaGC-MC can also utilize auxiliary data to enhance model performance.

INFORMATION SCIENCES (2023)

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Article Computer Science, Information Systems

EFFECT: Explainable framework for meta-learning in automatic classification algorithm selection

Xinyue Shao et al.

Summary: With the increasing convergence of AI and daily life scenarios, the application scenarios for intelligent decision methods are becoming more complex. Challenges include selecting the most suitable algorithm among many options and addressing the lack of interpretability in current machine learning algorithms. The proposed EFFECT framework aims to provide an interpretable meta-learning solution for autonomous algorithm selection.

INFORMATION SCIENCES (2023)

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Article Statistics & Probability

Interpretable machine learning: Fundamental principles and 10 grand challenges

Cynthia Rudin et al.

Summary: This work highlights the fundamental principles of interpretable machine learning and identifies 10 technical challenge areas in this field, including optimizing sparse models, scoring systems, and adding constraints for better interpretability. It serves as a useful starting point for statisticians and computer scientists interested in interpretable machine learning.

STATISTICS SURVEYS (2022)

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Article Psychology, Biological

Building machines that learn and think like people

Brenden M. Lake et al.

BEHAVIORAL AND BRAIN SCIENCES (2017)

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Neural architecture search with interpretable meta-features and fast predictors

相关参考文献

MetaGC-MC: A graph-based meta-learning approach to cold-start recommendation with/without auxiliary information

EFFECT: Explainable framework for meta-learning in automatic classification algorithm selection

Interpretable machine learning: Fundamental principles and 10 grand challenges

Building machines that learn and think like people

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