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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2003 / Cilt: 3 - Sayı: 1

Modular and hierarchical specialization in neural networks

A. J. W.M. Ten BERG
L. SPAANENBURG
Pages
719–726
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Further we illustrate that a deliberate mix of hierarchically imposed evaluation functions will improve network accuracy and learning speed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Modularity and hierarchy are fundamental notions in structured system design.
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Key Findings & Data:By subdividing a large and unstructured problem into smaller and tractable chunks, design automation becomes possible.
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Conclusion & Impact:Further we illustrate that a deliberate mix of hierarchically imposed evaluation functions will improve network accuracy and learning speed.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Modularity and hierarchy are fundamental notions in structured system design. By subdividing a large and unstructured problem into smaller and tractable chunks, design automation becomes possible. In this paper we discuss the use of modularity and hierarchy for functional specialization during the development of neural networks. We study the behavioral differences and requirements for back-propagation training of feed-forward networks. Further we illustrate that a deliberate mix of hierarchically imposed evaluation functions will improve network accuracy and learning speed.

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Cite This Article

BERG, A. J. W. T., SPAANENBURG, L. (2022). Modular and hierarchical specialization in neural networks. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 3 - Sayı: 1, 719–726. https://ajindex.com/articles/543913

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