Exploiting data parallelism in artificial neural networks with Haskell

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Title: Exploiting data parallelism in artificial neural networks with Haskell
Author: Heartsfield, Gregory Lynn
Abstract: 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 .
URI: http : / /hdl .handle .net /2152 /ETD -UT -2009 -08 -280
Date: 2010-06-04

Citation

Exploiting data parallelism in artificial neural networks with Haskell. Master's thesis, The University of Texas at Austin. Available electronically from http : / /hdl .handle .net /2152 /ETD -UT -2009 -08 -280 .

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