Introducing invariance: a principled approach to weight sharing
Introducing invariance: a principled approach to weight sharing
This paper appears in: Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on Meeting Date: 06/27/1994 -07/02/1994 Publication Date: 27 Jun-2 Jul 1994 Location: Orlando, FL , USA On page(s): 345-349 vol.1 Volume: 1, References Cited: 11 The paper describes a framework for addressing the training problem of multi-layer perceptrons by a principled introduction of weight sharing. The technique not only reduces the size of the class from which the learning algorithm must select its hypothesis but also reduces the number of examples required for a given level of generalization. The question of assessing the functionality of the weight sharing network is addressed, with a view to ensuring that the weight constraints introduced have not excluded the target functions of the learning task
345-349
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
1994
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Shawe-Taylor, John
(1994)
Introducing invariance: a principled approach to weight sharing.
In Proceedings of the IEEE International Conference on Neural Networks, Volume I, IEEE World Congress on Computational Intelligence, Orlando.
.
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Abstract
This paper appears in: Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on Meeting Date: 06/27/1994 -07/02/1994 Publication Date: 27 Jun-2 Jul 1994 Location: Orlando, FL , USA On page(s): 345-349 vol.1 Volume: 1, References Cited: 11 The paper describes a framework for addressing the training problem of multi-layer perceptrons by a principled introduction of weight sharing. The technique not only reduces the size of the class from which the learning algorithm must select its hypothesis but also reduces the number of examples required for a given level of generalization. The question of assessing the functionality of the weight sharing network is addressed, with a view to ensuring that the weight constraints introduced have not excluded the target functions of the learning task
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Published date: 1994
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Commentary On: IEEE
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Electronics & Computer Science
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Local EPrints ID: 259699
URI: http://eprints.soton.ac.uk/id/eprint/259699
PURE UUID: c365e24e-6a32-4dba-a6ca-8c33409d3421
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Date deposited: 10 Aug 2004
Last modified: 10 Dec 2021 21:06
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John Shawe-Taylor
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