Neural Networks Sequential Training Using Variational Gaussian Particle Filter

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Halimeh MM, Brendel A, Kellermann W
Publication year: 2019
Conference Proceedings Title: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Language: English


Abstract

In this paper, we propose a sequential training algorithm for
feed-forward neural networks based on particle filtering. The proposed
algorithm uses variational learning to tailor a proposal density by
minimizing the variational energy. This density is then incorporated
into the Gaussian particle filter framework. The proposed algorithm and
an extension to it using evolutionary resampling are compared to
training a neural network using a random walk-based particle filter, an
extended Kalman filter, the use of variational learning only, and the
backpropagation algorithm, using a synthetic dataset generated by a
time-varying random process and a real dataset, where the proposed
approach resulted in a moderately lower training and testing errors and a
better convergence behavior, rendering the algorithm attractive for
uses such as neural networks pre-training.


FAU Authors / FAU Editors

Brendel, Andreas
Professur für Nachrichtentechnik
Halimeh, MHD Modar
Lehrstuhl für Digitale Übertragung
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Halimeh, M.M., Brendel, A., & Kellermann, W. (2019). Neural Networks Sequential Training Using Variational Gaussian Particle Filter. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, GB.

MLA:
Halimeh, MHD Modar, Andreas Brendel, and Walter Kellermann. "Neural Networks Sequential Training Using Variational Gaussian Particle Filter." Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton 2019.

BibTeX: 

Last updated on 2019-02-07 at 09:38