{"id":"https://openalex.org/W2345441929","doi":"https://doi.org/10.1109/ijcnn.2017.7965940","title":"LSTM with working memory","display_name":"LSTM with working memory","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2345441929","doi":"https://doi.org/10.1109/ijcnn.2017.7965940","mag":"2345441929"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7965940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1605.01988","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039033196","display_name":"Andrew Pulver","orcid":null},"institutions":[{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew Pulver","raw_affiliation_strings":["Department of Computer Science, University at Albany","Univ. at Albany/SUNY"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University at Albany","institution_ids":["https://openalex.org/I392282"]},{"raw_affiliation_string":"Univ. at Albany/SUNY","institution_ids":["https://openalex.org/I392282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023752172","display_name":"Siwei Lyu","orcid":"https://orcid.org/0000-0002-0992-685X"},"institutions":[{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siwei Lyu","raw_affiliation_strings":["Department of Computer Science, University at Albany","Univ. at Albany/SUNY"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University at Albany","institution_ids":["https://openalex.org/I392282"]},{"raw_affiliation_string":"Univ. at Albany/SUNY","institution_ids":["https://openalex.org/I392282"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039033196"],"corresponding_institution_ids":["https://openalex.org/I392282"],"apc_list":null,"apc_paid":null,"fwci":0.23585868,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52356944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"845","last_page":"851"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12326","display_name":"Network Packet Processing and Optimization","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8693968057632446},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8402627110481262},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.7435488104820251},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7104313373565674},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6903228163719177},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5528596639633179},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4824683964252472},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35785454511642456},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.35395121574401855},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18615052103996277}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8693968057632446},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8402627110481262},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.7435488104820251},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7104313373565674},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6903228163719177},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5528596639633179},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4824683964252472},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35785454511642456},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.35395121574401855},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18615052103996277},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn.2017.7965940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1605.01988","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.01988","pdf_url":"https://arxiv.org/pdf/1605.01988","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2345441929","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1605.01988.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1605.01988","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1605.01988","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1605.01988","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.01988","pdf_url":"https://arxiv.org/pdf/1605.01988","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W581956982","https://openalex.org/W1026270304","https://openalex.org/W1484210532","https://openalex.org/W1522301498","https://openalex.org/W1771459135","https://openalex.org/W1800356822","https://openalex.org/W1924770834","https://openalex.org/W2018970719","https://openalex.org/W2064675550","https://openalex.org/W2107878631","https://openalex.org/W2136848157","https://openalex.org/W2140019974","https://openalex.org/W2147568880","https://openalex.org/W2157331557","https://openalex.org/W2175402905","https://openalex.org/W2178103884","https://openalex.org/W2294658705","https://openalex.org/W2325237720","https://openalex.org/W2950414499","https://openalex.org/W2950527759","https://openalex.org/W6616837769","https://openalex.org/W6682889407","https://openalex.org/W6696775858"],"related_works":["https://openalex.org/W2964211630","https://openalex.org/W581956982","https://openalex.org/W3033318292","https://openalex.org/W2790662531","https://openalex.org/W3036803174","https://openalex.org/W2908156223","https://openalex.org/W3013685564","https://openalex.org/W3014997656","https://openalex.org/W2901701848","https://openalex.org/W2099052794","https://openalex.org/W3015357480","https://openalex.org/W2952518099","https://openalex.org/W1499864241","https://openalex.org/W3015238143","https://openalex.org/W3162668143","https://openalex.org/W2912727508","https://openalex.org/W2787277569","https://openalex.org/W2952026396","https://openalex.org/W2896827527","https://openalex.org/W2951034016"],"abstract_inverted_index":{"Previous":[0],"RNN":[1,84],"architectures":[2],"have":[3,17],"largely":[4],"been":[5,18],"superseded":[6],"by":[7],"LSTM,":[8],"or":[9],"\"Long":[10],"Short-Term":[11],"Memory\".":[12],"Since":[13],"its":[14],"introduction,":[15],"there":[16],"many":[19],"variations":[20],"on":[21,72],"this":[22,55],"simple":[23,51,67],"design.":[24],"However,":[25],"it":[26],"is":[27,65],"still":[28,48,66],"widely":[29],"used":[30],"and":[31,52,68,92],"we":[32,57,76],"are":[33],"not":[34],"aware":[35],"of":[36],"a":[37,44,59,82],"gated-RNN":[38],"architecture":[39,64],"that":[40,75,87],"outperforms":[41],"LSTM":[42],"in":[43],"broad":[45],"sense":[46],"while":[47],"being":[49],"as":[50],"efficient.":[53],"In":[54],"paper":[56],"propose":[58],"modified":[60],"LSTM-like":[61],"architecture.":[62],"Our":[63],"achieves":[69],"better":[70],"performance":[71,85],"the":[73,89],"tasks":[74],"tested":[77],"on.":[78],"We":[79],"also":[80],"introduce":[81],"new":[83],"benchmark":[86],"uses":[88],"handwritten":[90],"digits":[91],"stresses":[93],"several":[94],"important":[95],"network":[96],"capabilities.":[97]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
