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Attention to Deep Structure in Recur...

University of Wyoming.

Attention to Deep Structure in Recurrent Neural Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Attention to Deep Structure in Recurrent Neural Networks./
作者:	Sharpe, Spencer S.
面頁冊數:	1 online resource (62 pages)
附註:	Source: Masters Abstracts International, Volume: 57-01.
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9780355537383

Attention to Deep Structure in Recurrent Neural Networks.
Sharpe, Spencer S.

Attention to Deep Structure in Recurrent Neural Networks. - 1 online resource (62 pages)

Source: Masters Abstracts International, Volume: 57-01.

Thesis (M.S.)--University of Wyoming, 2017.

Includes bibliographical references

Deep recurrent networks can build complex representations of sequential data, enabling them to do things such as translate text from one language into another. These networks often utilize an attention mechanism that allows them to focus on important input representations. Network attention can be used to provide insight into network strategies for identifying structure in the input sequence. This study investigates attention in a deep recurrent network, trained to annotate text, to determine how the distribution of information in the text affects learning and network performance. Results suggest that reversing the input text makes it difficult for the network to discover higher-order linguistic structure. This study contributes to an understanding of how our brains might process sequential data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355537383Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Attention to Deep Structure in Recurrent Neural Networks.
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500 $a Source: Masters Abstracts International, Volume: 57-01.
500 $a Advisers: Cameron H.G. Wright; Steve Barrett.
502 $a Thesis (M.S.)--University of Wyoming, 2017.
504 $a Includes bibliographical references
520 $a Deep recurrent networks can build complex representations of sequential data, enabling them to do things such as translate text from one language into another. These networks often utilize an attention mechanism that allows them to focus on important input representations. Network attention can be used to provide insight into network strategies for identifying structure in the input sequence. This study investigates attention in a deep recurrent network, trained to annotate text, to determine how the distribution of information in the text affects learning and network performance. Results suggest that reversing the input text makes it difficult for the network to discover higher-order linguistic structure. This study contributes to an understanding of how our brains might process sequential data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Information science. $3 561178
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0984
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710 2 $a University of Wyoming. $b Neuroscience. $3 1184407
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