Stochastic Connection Neural Networks
Stochastic Connection Neural Networks
We investigate a novel neural network model which uses stochastic weights. It is shown that the functionality of the network is comparable to that of a general stochastic neural network using standard sigmoid activation functions. For the multilayer feedforward structure we demonstrate the network can be successfully used to solve a real problem like handwritten digit recognition. It is also shown that the recurrent network is as powerful as a Boltzmann machine. A new technique to implement simulated annealing is presented. Simulation results on the graph bisection problem demonstrate the model is efficient for global optimization.
0 85296 641 5
35-39
IET Conference Publications
Zhao, Jieyu
2b47e359-c555-4247-89ee-d8430700d658
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
1995
Zhao, Jieyu
2b47e359-c555-4247-89ee-d8430700d658
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Zhao, Jieyu and Shawe-Taylor, John
(1995)
Stochastic Connection Neural Networks.
In Proceedings of the Fourth IEE Conference on Artificial Neural Networks.
IET Conference Publications.
.
(doi:10.1049/cp:19950525).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We investigate a novel neural network model which uses stochastic weights. It is shown that the functionality of the network is comparable to that of a general stochastic neural network using standard sigmoid activation functions. For the multilayer feedforward structure we demonstrate the network can be successfully used to solve a real problem like handwritten digit recognition. It is also shown that the recurrent network is as powerful as a Boltzmann machine. A new technique to implement simulated annealing is presented. Simulation results on the graph bisection problem demonstrate the model is efficient for global optimization.
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Published date: 1995
Venue - Dates:
Fourth International Conference on Artificial Neural Networks, Cambridge, UK, 1995-06-26 - 1995-06-28
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 259685
URI: http://eprints.soton.ac.uk/id/eprint/259685
ISBN: 0 85296 641 5
PURE UUID: 7c1e54dc-d363-4cb5-9232-0a547c1c8f78
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Date deposited: 05 Aug 2004
Last modified: 14 Mar 2024 06:27
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Contributors
Author:
Jieyu Zhao
Author:
John Shawe-Taylor
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