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Title:
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Exploiting data parallelism in artificial neural networks with Haskell |
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Author:
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Heartsfield, Gregory Lynn |
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Abstract:
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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 and elegance in this method , and determine that a sufficiently large workload is critical in achieving real gains . Several additional features are recommended to increase usability and improve results on small datasets . |
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URI:
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http : / /hdl .handle .net /2152 /ETD -UT -2009 -08 -280
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Date:
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2010-06-04 |