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A local training-pruning approach for recurrent neural networks.

Chi-Sing Leung1, Ping-Man Lam

  • 1The City University of Hong Kong, Kowloon Tong, Hong Kong, China. eeleungc@cityu.edu.hk

International Journal of Neural Systems
|March 15, 2003
PubMed
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We introduce a local Extended Kalman Filtering (EKF) training-pruning method for recurrent neural networks (RNNs). This approach reduces computational cost and storage, making RNNs more practical for real-world online applications.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Global Extended Kalman Filtering (EKF) for Recurrent Neural Networks (RNNs) faces challenges with high computational and storage demands.
  • Existing methods limit the practical application of RNNs in resource-constrained environments.

Purpose of the Study:

  • To develop an efficient training and pruning method for RNNs.
  • To address the computational and storage limitations of global EKF algorithms.
  • To enhance the practicality of RNNs for real-world online operations.

Main Methods:

  • Proposed a local EKF training-pruning approach for RNNs.
  • Utilized by-products from local EKF training to determine network weight importance.
  • Implemented a joint training and pruning strategy.

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Main Results:

  • The local EKF approach significantly reduces computational cost and storage requirements compared to global methods.
  • Demonstrated effectiveness as a joint training-pruning method for RNNs.
  • Achieved practical applicability for RNNs in online settings.

Conclusions:

  • The local EKF training-pruning method offers a computationally efficient and storage-friendly alternative for RNNs.
  • This approach enhances the feasibility of deploying RNNs in real-world scenarios.
  • The method is effective for online RNN operation and model optimization.