{"id":"https://openalex.org/W2020408662","doi":"https://doi.org/10.1109/slt.2014.7078561","title":"Deep Order Statistic Networks","display_name":"Deep Order Statistic Networks","publication_year":2014,"publication_date":"2014-12-01","ids":{"openalex":"https://openalex.org/W2020408662","doi":"https://doi.org/10.1109/slt.2014.7078561","mag":"2020408662"},"language":"en","primary_location":{"id":"doi:10.1109/slt.2014.7078561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt.2014.7078561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Spoken Language Technology Workshop (SLT)","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/A5088753951","display_name":"Steven J. Rennie","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]},{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Steven J. Rennie","raw_affiliation_strings":["IBM Thomas J. Watson Research Center","IBM Thomas J.Watson Research Center, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM Thomas J.Watson Research Center, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034451965","display_name":"Vaibhava Goel","orcid":"https://orcid.org/0000-0002-5504-3863"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vaibhava Goel","raw_affiliation_strings":["IBM Thomas J. Watson Research Center","IBM Thomas J.Watson Research Center, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM Thomas J.Watson Research Center, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101787514","display_name":"Samuel Thomas","orcid":"https://orcid.org/0000-0001-7573-0620"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samuel Thomas","raw_affiliation_strings":["IBM Thomas J. Watson Research Center","IBM Thomas J.Watson Research Center, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM Thomas J.Watson Research Center, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088753951"],"corresponding_institution_ids":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"],"apc_list":null,"apc_paid":null,"fwci":3.3761,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.92960444,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"124","last_page":"128"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9983999729156494,"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/T10320","display_name":"Neural Networks and Applications","score":0.9983999729156494,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.998199999332428,"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/T10860","display_name":"Speech and Audio Processing","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/dropout","display_name":"Dropout (neural networks)","score":0.7970401644706726},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7701850533485413},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.6901707649230957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5771327614784241},{"id":"https://openalex.org/keywords/linearity","display_name":"Linearity","score":0.5652731657028198},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5455474853515625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5362851619720459},{"id":"https://openalex.org/keywords/linear-interpolation","display_name":"Linear interpolation","score":0.5126585364341736},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.4418044686317444},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4347989857196808},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4232054352760315},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3977072238922119},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3526340126991272},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.25433385372161865},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17124027013778687},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11775541305541992},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08246147632598877}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7970401644706726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7701850533485413},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.6901707649230957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5771327614784241},{"id":"https://openalex.org/C77170095","wikidata":"https://www.wikidata.org/wiki/Q1753188","display_name":"Linearity","level":2,"score":0.5652731657028198},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5455474853515625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5362851619720459},{"id":"https://openalex.org/C171836373","wikidata":"https://www.wikidata.org/wiki/Q2266329","display_name":"Linear interpolation","level":3,"score":0.5126585364341736},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.4418044686317444},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4347989857196808},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4232054352760315},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3977072238922119},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3526340126991272},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25433385372161865},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17124027013778687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11775541305541992},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08246147632598877},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/slt.2014.7078561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt.2014.7078561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Spoken Language Technology Workshop (SLT)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.676.191","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.676.191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.clsp.jhu.edu/%7Esamuel/pdfs/deep_order_stats.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W104847522","https://openalex.org/W1524333225","https://openalex.org/W1533072162","https://openalex.org/W1904365287","https://openalex.org/W1979482308","https://openalex.org/W2026369565","https://openalex.org/W2042141988","https://openalex.org/W2062164080","https://openalex.org/W2062227835","https://openalex.org/W2089917322","https://openalex.org/W2110176223","https://openalex.org/W2112739286","https://openalex.org/W2120480077","https://openalex.org/W2147800946","https://openalex.org/W2155117693","https://openalex.org/W2294059674","https://openalex.org/W2405787239","https://openalex.org/W2950789693","https://openalex.org/W6631362777"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W1521968289","https://openalex.org/W2782041652","https://openalex.org/W2952088488","https://openalex.org/W2612657834","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W2481230473","https://openalex.org/W2792147139","https://openalex.org/W2382946128"],"abstract_inverted_index":{"Recently,":[0],"Maxout":[1,18,73,124],"networks":[2,19,63,125],"have":[3],"demonstrated":[4],"state-of-the-art":[5],"performance":[6,137],"on":[7,17,82],"several":[8],"machine":[9],"learning":[10],"tasks,":[11],"which":[12,52],"has":[13],"fueled":[14],"aggressive":[15],"research":[16],"and":[20,88,95,110],"generalizations":[21,71],"thereof.":[22],"In":[23],"this":[24],"work,":[25],"we":[26],"propose":[27],"the":[28,37,49,76,79,83,86,93,117,132],"utilization":[29],"of":[30,36,44,72,78,119,138],"order":[31,120],"statistics":[32,121],"as":[33,106],"a":[34],"generalization":[35],"max":[38],"non-linearity.":[39],"A":[40],"particularly":[41],"general":[42],"example":[43],"an":[45],"order-statistic":[46],"non-linearity":[47],"is":[48],"\u201csortout\u201d":[50],"non-linearity,":[51],"outputs":[53],"all":[54],"input":[55],"activations,":[56],"but":[57],"in":[58,65,131],"sorted":[59],"order.":[60],"Such":[61],"Order-statistic":[62],"(OSNs),":[64],"contrast":[66],"with":[67],"other":[68],"recently":[69],"proposed":[70],"networks,":[74],"leave":[75],"determination":[77],"interpolation":[80],"weights":[81],"activations":[84],"to":[85,128],"network,":[87],"remain":[89],"conditionally":[90],"linear":[91],"given":[92],"input,":[94],"so":[96],"are":[97],"well":[98],"suited":[99],"for":[100],"powerful":[101],"model":[102],"aggregation":[103],"techniques":[104],"such":[105],"dropout,":[107],"drop":[108],"connect,":[109],"annealed":[111],"dropout.":[112],"Experimental":[113],"results":[114],"demonstrate":[115],"that":[116],"use":[118],"rather":[122],"than":[123],"can":[126],"lead":[127],"substantial":[129],"improvements":[130],"word":[133],"error":[134],"rate":[135],"(WER)":[136],"automatic":[139],"speech":[140],"recognition":[141],"systems.":[142]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
