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Related Experiment Video

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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

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Published on: May 25, 2019

On the Kalman filtering method in neural network training and pruning.

J Sum¹, C S Leung, G H Young

¹Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This study presents methods for setting initial conditions and using results from the extended Kalman filter (EKF) for training and pruning feedforward neural networks. It introduces an equation connecting error sensitivity to EKF results for effective neural network pruning.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Training and pruning feedforward neural networks using the extended Kalman filter (EKF) presents challenges in setting initial conditions and utilizing results for pruning.
Existing methods lack clear guidance on initial condition selection and effective EKF result application for network optimization.

Purpose of the Study:

To provide guidance on setting initial conditions for EKF-based neural network training.
To develop a method for pruning neural networks using EKF results by establishing a link between error sensitivity and EKF outputs.
To validate the proposed method through simulation.

Main Methods:

The study illustrates methods for setting initial conditions with a simple example.
An equation is derived that links error sensitivity (saliency) to EKF results under specific assumptions (large training set, convergence, model accuracy).
The derived equation's validity is tested using a simulated example.

Main Results:

The paper offers practical cues for initializing the extended Kalman filter in neural network training.
A novel equation is presented that connects the saliency of network parameters to EKF-derived information.
The developed equation is empirically validated through a simulated network pruning scenario.

Conclusions:

The study provides a framework for effectively utilizing the extended Kalman filter in neural network pruning.
The derived equation offers a computationally efficient way to identify and remove redundant network components.
The findings contribute to more efficient and effective neural network optimization techniques.