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Pruning recurrent neural networks for improved generalization performance.

C L Giles¹, C W Omlin

¹NEC Res. Inst., Princeton, NJ.

IEEE Transactions on Neural Networks

|January 1, 1994

Summary

This study introduces a pruning heuristic to optimize recurrent neural network architecture. This method enhances generalization performance and extracts more consistent rules from trained networks.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Determining optimal neural network architecture, particularly for recurrent neural networks (RNNs), remains a challenge.
Existing methods lack general approaches for estimating key architectural parameters like hidden layers, neuron counts, or weight sizes.
Poor architectural choices can hinder generalization performance in trained models.

Purpose of the Study:

To present a novel, simple pruning heuristic for recurrent neural networks.
To improve the generalization performance of trained recurrent neural networks.
To demonstrate that extracted rules from pruned networks align better with target grammar rules.

Main Methods:

A pruning heuristic was developed and applied to fully recurrent neural networks.
Networks were trained on positive and negative strings of regular grammars.
The heuristic involved pruning and retraining the networks to refine their architecture.
Simulations were conducted on a 10-state random grammar and an 8-state triple-parity grammar.

Main Results:

The pruning heuristic significantly improved the generalization performance of trained recurrent neural networks.
Rules extracted from networks trained with the heuristic were more consistent with the learned grammar rules.
The pruning method demonstrated superior generalization performance compared to traditional weight decay techniques.
Effectiveness was validated on two distinct regular grammars.

Conclusions:

The proposed pruning heuristic offers an effective strategy for optimizing recurrent neural network architecture.
This method enhances model generalization and the interpretability of learned rules.
The heuristic provides a valuable alternative to existing regularization techniques like weight decay.