Generating Sequences With Recurrent Neural Networks. Graves, A. 2013. ![Generating Sequences With Recurrent Neural Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.
@article{
title = {Generating Sequences With Recurrent Neural Networks},
type = {article},
year = {2013},
pages = {1-43},
websites = {http://arxiv.org/abs/1308.0850},
id = {5bb177b5-1292-3b75-aaf0-b51ad23d35eb},
created = {2021-07-12T14:15:35.512Z},
file_attached = {true},
profile_id = {ad172e55-c0e8-3aa4-8465-09fac4d5f5c8},
group_id = {1ff583c0-be37-34fa-9c04-73c69437d354},
last_modified = {2021-07-12T14:17:17.286Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
folder_uuids = {85ed9c29-c272-40dc-a01a-f912101de83a},
private_publication = {false},
abstract = {This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.},
bibtype = {article},
author = {Graves, Alex}
}
Downloads: 0
{"_id":"4AQmnJ9EHRtoRjEuK","bibbaseid":"graves-generatingsequenceswithrecurrentneuralnetworks-2013","authorIDs":[],"author_short":["Graves, A."],"bibdata":{"title":"Generating Sequences With Recurrent Neural Networks","type":"article","year":"2013","pages":"1-43","websites":"http://arxiv.org/abs/1308.0850","id":"5bb177b5-1292-3b75-aaf0-b51ad23d35eb","created":"2021-07-12T14:15:35.512Z","file_attached":"true","profile_id":"ad172e55-c0e8-3aa4-8465-09fac4d5f5c8","group_id":"1ff583c0-be37-34fa-9c04-73c69437d354","last_modified":"2021-07-12T14:17:17.286Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"folder_uuids":"85ed9c29-c272-40dc-a01a-f912101de83a","private_publication":false,"abstract":"This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.","bibtype":"article","author":"Graves, Alex","bibtex":"@article{\n title = {Generating Sequences With Recurrent Neural Networks},\n type = {article},\n year = {2013},\n pages = {1-43},\n websites = {http://arxiv.org/abs/1308.0850},\n id = {5bb177b5-1292-3b75-aaf0-b51ad23d35eb},\n created = {2021-07-12T14:15:35.512Z},\n file_attached = {true},\n profile_id = {ad172e55-c0e8-3aa4-8465-09fac4d5f5c8},\n group_id = {1ff583c0-be37-34fa-9c04-73c69437d354},\n last_modified = {2021-07-12T14:17:17.286Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {85ed9c29-c272-40dc-a01a-f912101de83a},\n private_publication = {false},\n abstract = {This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.},\n bibtype = {article},\n author = {Graves, Alex}\n}","author_short":["Graves, A."],"urls":{"Website":"http://arxiv.org/abs/1308.0850"},"biburl":"https://bibbase.org/service/mendeley/bfbbf840-4c42-3914-a463-19024f50b30c","bibbaseid":"graves-generatingsequenceswithrecurrentneuralnetworks-2013","role":"author","metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/bfbbf840-4c42-3914-a463-19024f50b30c","creationDate":"2020-01-27T02:13:34.310Z","downloads":0,"keywords":[],"search_terms":["generating","sequences","recurrent","neural","networks","graves"],"title":"Generating Sequences With Recurrent Neural Networks","year":2013,"dataSources":["hEoKh4ygEAWbAZ5iy","ya2CyA73rpZseyrZ8","XFrKPG99s5t3W7xuW","Wsv2bQ4jPuc7qme8R","2252seNhipfTmjEBQ"]}