{"id":"https://openalex.org/W3201250149","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533766","title":"OrderNet: Sorting High Dimensional Low Sample Data with Few-Shot Learning","display_name":"OrderNet: Sorting High Dimensional Low Sample Data with Few-Shot Learning","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201250149","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533766","mag":"3201250149"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102843280","display_name":"Samuel T. Hess","orcid":"https://orcid.org/0000-0003-2359-6540"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samuel Hess","raw_affiliation_strings":["University of Arizona,Department of Electrical and Computer Engineering,Tucson,AZ,85721"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona,Department of Electrical and Computer Engineering,Tucson,AZ,85721","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079638101","display_name":"Gregory Ditzler","orcid":"https://orcid.org/0000-0001-6890-0935"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory Ditzler","raw_affiliation_strings":["University of Arizona,Department of Electrical and Computer Engineering,Tucson,AZ,85721"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona,Department of Electrical and Computer Engineering,Tucson,AZ,85721","institution_ids":["https://openalex.org/I138006243"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I138006243"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"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/T10320","display_name":"Neural Networks and Applications","score":0.9980000257492065,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"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":0.7830106019973755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7474852800369263},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.7030816078186035},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6670475006103516},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6328176856040955},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5876942276954651},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.5490767955780029},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4897775650024414},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47324925661087036},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.47120827436447144},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4444366693496704},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4405606985092163},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.42103075981140137},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3688185214996338},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09158056974411011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7830106019973755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7474852800369263},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.7030816078186035},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6670475006103516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6328176856040955},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5876942276954651},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.5490767955780029},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4897775650024414},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47324925661087036},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.47120827436447144},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4444366693496704},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4405606985092163},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.42103075981140137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3688185214996338},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09158056974411011},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5261017774","display_name":"CAREER: Learning in Adversarial and Nonstationary Environments","funder_award_id":"1943552","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7861850912","display_name":null,"funder_award_id":"DE-NA0003946","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2115733720","https://openalex.org/W2127589108","https://openalex.org/W2144656844","https://openalex.org/W2325963232","https://openalex.org/W2401823607","https://openalex.org/W2592232824","https://openalex.org/W2601450892","https://openalex.org/W2753160622","https://openalex.org/W2770173563","https://openalex.org/W2797807814","https://openalex.org/W2803176509","https://openalex.org/W2805481182","https://openalex.org/W2913318911","https://openalex.org/W2914331073","https://openalex.org/W2921353139","https://openalex.org/W2922386288","https://openalex.org/W2963341924","https://openalex.org/W2963741406","https://openalex.org/W2963845150","https://openalex.org/W2963975576","https://openalex.org/W2964121937","https://openalex.org/W3034942609","https://openalex.org/W3083228912","https://openalex.org/W3091905774","https://openalex.org/W3100789280","https://openalex.org/W4206723194","https://openalex.org/W4287671529","https://openalex.org/W4297812687","https://openalex.org/W4300514939","https://openalex.org/W6681389334","https://openalex.org/W6713057566","https://openalex.org/W6717697761","https://openalex.org/W6735236233","https://openalex.org/W6741533928","https://openalex.org/W6743661861","https://openalex.org/W6746638498","https://openalex.org/W6750476862","https://openalex.org/W6751655026","https://openalex.org/W6751959828","https://openalex.org/W6766092863","https://openalex.org/W6783596713","https://openalex.org/W6840720034"],"related_works":["https://openalex.org/W2497720472","https://openalex.org/W4292659306","https://openalex.org/W4287637665","https://openalex.org/W3044321615","https://openalex.org/W4294892107","https://openalex.org/W2806221744","https://openalex.org/W2326937258","https://openalex.org/W394267150","https://openalex.org/W2988685434","https://openalex.org/W2357748469"],"abstract_inverted_index":{"Neural":[0],"networks":[1,24,57,61],"have":[2,29,44,143],"shown":[3],"remarkable":[4],"classification":[5,60,64],"performances":[6],"in":[7,13,240],"recent":[8],"years,":[9],"often":[10],"outperforming":[11],"humans":[12],"many":[14,173],"tasks.":[15],"Unfortunately,":[16],"there":[17],"are":[18,79],"some":[19],"tasks":[20,65],"where":[21],"conventional":[22,236],"neural":[23,53,56,187,238],"and":[25,150,171],"their":[26],"training":[27,151],"methods":[28],"under-performed.":[30],"One":[31],"of":[32,41,51,86,105,124,164,180,212,221],"these":[33,76],"areas":[34],"is":[35,184],"learning":[36,63,91,100,106,125],"from":[37,108,126,200],"a":[38,70,83,109,137,146,156,235],"small":[39,158],"number":[40,85],"samples":[42,72],"which":[43],"been":[45],"partially":[46],"addressed":[47],"with":[48,66,68,82],"the":[49,95,103,122,134,178,185,191,241],"development":[50],"few-shot":[52,77,90,201],"networks.":[54],"Few-shot":[55],"contrast":[58],"traditional":[59],"by":[62,132,215,224],"datasets":[67],"only":[69,93],"few":[71],"per":[73,98],"class;":[74],"however,":[75],"techniques":[78,198],"trained":[80],"collectively":[81],"large":[84],"labeled":[87],"samples.":[88,176],"Hence,":[89],"approaches":[92],"address":[94,121,190],"low":[96,110,129,168,194,242],"sample":[97,111,130,169,195,243],"class":[99],"problem":[101,135],"whereas":[102],"task":[104],"strictly":[107],"size":[112],"still":[113],"goes":[114],"mostly":[115],"unresolved.":[116],"In":[117],"this":[118],"contribution,":[119],"we":[120,142,227],"challenge":[123],"high":[127,166,192],"dimensional":[128,167,193],"data":[131,138,170,196],"revising":[133],"into":[136],"ordering":[139],"task.":[140],"Specifically,":[141],"designed":[144],"OrderNet,":[145],"novel":[147],"network":[148,188,239],"design":[149],"approach":[152],"that":[153,229],"can":[154],"take":[155],"relatively":[157],"amount":[159],"(less":[160],"than":[161],"200":[162],"samples)":[163],"ordered":[165],"organize":[172],"more":[174],"unseen":[175],"To":[177],"best":[179],"our":[181],"knowledge,":[182],"OrderNet":[183,205,230],"first":[186],"to":[189,209],"using":[197],"adopted":[199],"learning.":[202],"We":[203],"evaluate":[204],"against":[206],"its":[207],"ability":[208],"order":[210],"images":[211,220],"analog":[213],"clocks":[214],"time":[216],"as":[217,219],"well":[218],"profile":[222],"pictures":[223],"age.":[225],"Additionally,":[226],"demonstrate":[228],"has":[231],"superior":[232],"performance":[233],"over":[234],"regression":[237],"regime.":[244]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
