{"id":"https://openalex.org/W2982011653","doi":"https://doi.org/10.1145/3362743.3362965","title":"Skipping RNN State Updates without Retraining the Original Model","display_name":"Skipping RNN State Updates without Retraining the Original Model","publication_year":2019,"publication_date":"2019-10-23","ids":{"openalex":"https://openalex.org/W2982011653","doi":"https://doi.org/10.1145/3362743.3362965","mag":"2982011653"},"language":"en","primary_location":{"id":"doi:10.1145/3362743.3362965","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3362743.3362965","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Machine Learning on Edge in Sensor Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101981714","display_name":"Jin Tao","orcid":"https://orcid.org/0000-0003-4437-1834"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jin Tao","raw_affiliation_strings":["Washington State University, Pullman, Washington"],"affiliations":[{"raw_affiliation_string":"Washington State University, Pullman, Washington","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090396089","display_name":"Urmish Thakker","orcid":"https://orcid.org/0000-0002-0515-9155"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Urmish Thakker","raw_affiliation_strings":["Arm ML Research Lab, Austin, Texas"],"affiliations":[{"raw_affiliation_string":"Arm ML Research Lab, Austin, Texas","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004292742","display_name":"Ganesh Dasika","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ganesh Dasika","raw_affiliation_strings":["Arm ML Research Lab, Austin, Texas"],"affiliations":[{"raw_affiliation_string":"Arm ML Research Lab, Austin, Texas","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013531730","display_name":"Jesse Beu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jesse Beu","raw_affiliation_strings":["Arm ML Research Lab, Austin, Texas"],"affiliations":[{"raw_affiliation_string":"Arm ML Research Lab, Austin, Texas","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101981714"],"corresponding_institution_ids":["https://openalex.org/I72951846"],"apc_list":null,"apc_paid":null,"fwci":2.1002,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.90489644,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"36"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9950000047683716,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.8577654361724854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8259737491607666},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.7092826962471008},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5893071889877319},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5164661407470703},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.46956944465637207},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.44488224387168884},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.4245634377002716},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27784883975982666},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17311424016952515}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.8577654361724854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8259737491607666},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.7092826962471008},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5893071889877319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5164661407470703},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.46956944465637207},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.44488224387168884},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.4245634377002716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27784883975982666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17311424016952515},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3362743.3362965","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3362743.3362965","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Machine Learning on Edge in Sensor Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W587794757","https://openalex.org/W1815076433","https://openalex.org/W2547418827","https://openalex.org/W2560017826","https://openalex.org/W2609900691","https://openalex.org/W2747579829","https://openalex.org/W2749590927","https://openalex.org/W2751366252","https://openalex.org/W2767693128","https://openalex.org/W2772956802","https://openalex.org/W2787146684","https://openalex.org/W2890744983","https://openalex.org/W2908385757","https://openalex.org/W2915589364","https://openalex.org/W2920046642","https://openalex.org/W2927608232","https://openalex.org/W2948954216","https://openalex.org/W2949896761","https://openalex.org/W2951305674","https://openalex.org/W2952344559","https://openalex.org/W3099559063","https://openalex.org/W3104393472","https://openalex.org/W6617368339","https://openalex.org/W6730047919","https://openalex.org/W6759263581"],"related_works":["https://openalex.org/W2006651773","https://openalex.org/W2027050655","https://openalex.org/W3028244590","https://openalex.org/W4254349500","https://openalex.org/W2014369232","https://openalex.org/W3122042562","https://openalex.org/W2050078012","https://openalex.org/W2060761133","https://openalex.org/W2360307734","https://openalex.org/W2028424651"],"abstract_inverted_index":{"Recurrent":[0],"Neural":[1],"Networks":[2],"(RNNs)":[3],"break":[4],"a":[5,9,23,91,122,172,184],"time-series":[6],"input":[7],"(or":[8,14],"sentence)":[10],"into":[11],"multiple":[12],"time-steps":[13,30,61,96],"words)":[15],"and":[16,72,133,178],"process":[17],"it":[18],"one":[19,77],"time-step":[20],"(word)":[21],"at":[22,204],"time.":[24],"However,":[25,66],"not":[26,64,154],"all":[27],"of":[28,55,83,127,135,181],"these":[29],"(words)":[31],"need":[32],"to":[33,36,59,78,93,124,151,195],"be":[34],"processed":[35],"determine":[37],"the":[38,56,70,73,81,84,102,116,130,138,143,149,176,190,197,215],"final":[39],"output":[40],"accurately.":[41],"Prior":[42],"work":[43,88],"has":[44],"exploited":[45],"this":[46,87],"intuition":[47],"by":[48,156,199],"incorporating":[49],"an":[50,107],"additional":[51],"predictor":[52,71,123,188],"in":[53,208],"front":[54],"RNN":[57,74,95,104,217],"model":[58,144],"prune":[60],"that":[62,112,148],"are":[63],"relevant.":[65],"they":[67],"jointly":[68],"train":[69,121],"model,":[75,118],"allowing":[76],"learn":[79],"from":[80,166],"mistakes":[82],"other.":[85],"In":[86],"we":[89,110,119,170],"present":[90,171],"method":[92],"skip":[94,125,152],"without":[97,114,141],"retraining":[98,115],"or":[99],"fine":[100],"tuning":[101],"original":[103,117,216],"model.":[105,218],"Using":[106],"ideal":[108],"predictor,":[109],"show":[111,147],"even":[113],"can":[120],"45%":[126],"steps":[128,136],"for":[129,137],"SST":[131,191],"dataset":[132,140,192],"80%":[134],"IMDB":[139],"impacting":[142],"accuracy.":[145],"We":[146],"decision":[150],"is":[153,193],"easy":[155],"comparing":[157],"against":[158],"5":[159],"different":[160],"baselines":[161],"based":[162],"on":[163,189],"solutions":[164],"derived":[165],"domain":[167],"knowledge.":[168],"Finally,":[169],"case":[173],"study":[174],"about":[175],"cost":[177],"accuracy":[179,209],"benefits":[180],"realizing":[182],"such":[183],"predictor.":[185],"This":[186],"realistic":[187],"able":[194],"reduce":[196],"computation":[198],"more":[200],"than":[201,214],"25%":[202],"with":[203],"most":[205],"0.3%":[206],"loss":[207],"while":[210],"being":[211],"40\u00d7":[212],"smaller":[213]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
