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Neural Transplant Surgery: An Approach to Pre-training Recurrent Networks
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Available under University of Tasmania Standard License.
Partially-recurrent networks have advantages over strictly feed-forward networks for certain spatiotemporal pattern classification or prediction tasks. However networks involving recurrent links are generally more difficult to train than their non-recurrent counterparts. In this paper we demonstrate that the costs of training a recurrent network can be greatly reduced by initialising the network prior to training with weights 'transplanted' from a non-recurrent architecture.
|Item Type:||Conference or Workshop Item (Paper)|
|Keywords:||recurrent neural networks, pre-training, weight initialisation|
|Date Deposited:||12 Aug 2004|
|Last Modified:||18 Nov 2014 03:10|
|Item Statistics:||View statistics for this item|
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