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Pruning artificial neural networks using neural complexity measures.

Thomas D Jorgensen¹, Barry P Haynes, Charlotte C F Norlund

¹Department of Electronic and Computer Engineering, University of Portsmouth, Portsmouth, United Kingdom. Thomas.Jorgensen@port.ac.uk

International Journal of Neural Systems

|November 11, 2008

Summary

This summary is machine-generated.

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This study introduces a novel neural network pruning method using information-theoretic complexity. The technique effectively reduces network size while preserving performance and improving learning speed, outperforming magnitude-based pruning.

Area of Science:

Artificial Intelligence
Computational Neuroscience
Machine Learning

Background:

Artificial neural networks (ANNs) often grow excessively large and complex during training.
Existing pruning methods, like Magnitude Based Pruning, aim to simplify networks but can impact performance.
A need exists for more effective pruning techniques that balance network size, complexity, and learned behavior.

Purpose of the Study:

To introduce a novel method for pruning artificial neural networks based on neural complexity.
To evaluate the efficacy of this new pruning technique in reducing network size while retaining learned behavior and fitness.
To compare the proposed method against Magnitude Based Pruning in a robot control task.

Main Methods:

A new measure of information-theoretic neural complexity was developed to identify connections for pruning.

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The proposed pruning method was applied to artificial neural networks in a simulated robot control domain (racecar simulation).

Performance was evaluated by comparing the pruned networks against the original benchmark network and networks pruned using Magnitude Based Pruning.

Main Results:

The novel pruning method successfully reduced the size of complex neural networks while maintaining their learned behavior and fitness.
The proposed technique demonstrated significant improvement over Magnitude Based Pruning in the robot control domain.
Some pruned networks exhibited faster learning capabilities than their original, unpruned counterparts, indicating reduced dimensionality benefits.

Conclusions:

The information-theoretic complexity-based pruning method offers an effective approach to optimize neural network size and complexity.
This novel pruning technique not only reduces network size but can also enhance learning efficiency and uncover hidden potential.
The method provides a way to discover network topologies that better match the complexity of the problem being solved.