Exploiting data parallelism in artificial neural networks with Haskell
Functional parallel programming techniques for feed-forward artificial neural networks trained using backpropagation learning are analyzed. In particular, the Data Parallel Haskell extension to the Glasgow Haskell Compiler is considered as a tool for achieving data parallelism. We find much potential...
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| Format: | Others |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/2152/ETD-UT-2009-08-280 |