{"id":"https://openalex.org/W2787091091","doi":"https://doi.org/10.18653/v1/d19-6120","title":"Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing","display_name":"Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2787091091","doi":"https://doi.org/10.18653/v1/d19-6120","mag":"2787091091"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d19-6120","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-6120","pdf_url":"https://www.aclweb.org/anthology/D19-6120.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D19-6120.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017183605","display_name":"Somnath Basu Roy Chowdhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Somnath Basu Roy Chowdhury","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051355265","display_name":"K. M. Annervaz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Annervaz M","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010292297","display_name":"Ambedkar Dukkipati","orcid":"https://orcid.org/0000-0002-6352-6283"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ambedkar Dukkipati","raw_affiliation_strings":["Indian Institute of Science"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5017183605"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.434,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70129429,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"183","last_page":"191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8174259662628174},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7061527967453003},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.7023612260818481},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6509410738945007},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6437987089157104},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.5249159932136536},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5161972641944885},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5147126317024231},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.5070257186889648},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4813838303089142},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.430130273103714},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.41754376888275146},{"id":"https://openalex.org/keywords/hash-table","display_name":"Hash table","score":0.11280256509780884}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8174259662628174},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7061527967453003},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.7023612260818481},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6509410738945007},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6437987089157104},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.5249159932136536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5161972641944885},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5147126317024231},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.5070257186889648},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4813838303089142},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.430130273103714},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.41754376888275146},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.11280256509780884},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.18653/v1/d19-6120","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-6120","pdf_url":"https://www.aclweb.org/anthology/D19-6120.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1802.05934","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.05934","pdf_url":"https://arxiv.org/pdf/1802.05934","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":null,"raw_type":"text"},{"id":"mag:2787091091","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1802.05934.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.1802.05934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1802.05934","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"},{"id":"doi:10.60692/4qwt7-y3t80","is_oa":true,"landing_page_url":"https://doi.org/10.60692/4qwt7-y3t80","pdf_url":null,"source":{"id":"https://openalex.org/S7407051682","display_name":"Greater South Information System","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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-journal"},{"id":"doi:10.60692/9t5fq-43z25","is_oa":true,"landing_page_url":"https://doi.org/10.60692/9t5fq-43z25","pdf_url":null,"source":{"id":"https://openalex.org/S7407051682","display_name":"Greater South Information System","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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-journal"}],"best_oa_location":{"id":"doi:10.18653/v1/d19-6120","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-6120","pdf_url":"https://www.aclweb.org/anthology/D19-6120.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6700000166893005,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2787091091.pdf","grobid_xml":"https://content.openalex.org/works/W2787091091.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W179892336","https://openalex.org/W1502916507","https://openalex.org/W1522301498","https://openalex.org/W1565201084","https://openalex.org/W1596717185","https://openalex.org/W1663973292","https://openalex.org/W1689711448","https://openalex.org/W2037046020","https://openalex.org/W2101234009","https://openalex.org/W2104643304","https://openalex.org/W2115575686","https://openalex.org/W2118190603","https://openalex.org/W2121227244","https://openalex.org/W2122922389","https://openalex.org/W2147717514","https://openalex.org/W2155541015","https://openalex.org/W2158899491","https://openalex.org/W2165698076","https://openalex.org/W2187089797","https://openalex.org/W2404336876","https://openalex.org/W2911964244","https://openalex.org/W2950361018","https://openalex.org/W3035219538","https://openalex.org/W3120740533"],"related_works":["https://openalex.org/W2982807168","https://openalex.org/W3138908442","https://openalex.org/W2589956745","https://openalex.org/W3093833448","https://openalex.org/W3210421444","https://openalex.org/W3160285281","https://openalex.org/W3111988074","https://openalex.org/W2151575489","https://openalex.org/W3091617927","https://openalex.org/W2949114386","https://openalex.org/W3033881428","https://openalex.org/W2963983343","https://openalex.org/W3029470851","https://openalex.org/W3166784900","https://openalex.org/W2106981652","https://openalex.org/W3198965343","https://openalex.org/W2895749405","https://openalex.org/W3011199201","https://openalex.org/W3118878482","https://openalex.org/W2962685835"],"abstract_inverted_index":{"Supervised":[0],"learning":[1,59,64,72,97,138,199,210],"models":[2,15,65],"are":[3,108],"typically":[4],"trained":[5],"on":[6,18,40,132,184],"a":[7,87,140,188,212],"single":[8,213],"dataset":[9,23,198],"and":[10,105,120,124,147],"the":[11,19,22,25,41,49,95,101,128,133,166,179],"performance":[12,208],"of":[13,21,27,100,103,111],"these":[14],"rely":[16],"heavily":[17],"size":[20],"i.e.,":[24],"amount":[26],"data":[28,42,186],"available":[29],"with":[30,47],"ground":[31],"truth.":[32],"Learning":[33],"algorithms":[34],"try":[35],"to":[36,82],"generalize":[37],"solely":[38],"based":[39],"that":[43,61,178,203],"it":[44],"is":[45,115],"presented":[46],"during":[48,130],"training.":[50],"In":[51],"this":[52,158],"work,":[53],"we":[54,143,160,201],"propose":[55,81],"an":[56,154],"inductive":[57],"transfer":[58],"method":[60],"can":[62,205],"augment":[63],"by":[66,187],"infusing":[67],"similar":[68],"instances":[69,102,114],"from":[70,86,94,139,153,211],"different":[71],"tasks":[73],"in":[74],"Natural":[75],"Language":[76],"Processing":[77],"(NLP)":[78],"domain.":[79],"We":[80],"use":[83],"instance":[84],"representations":[85],"source":[88,96,104,113],"dataset,":[89],"without":[90],"inheriting":[91],"anything":[92],"else":[93],"model.":[98],"Representations":[99],"target":[106,134],"datasets":[107],"learned,":[109],"retrieval":[110],"relevant":[112,149],"performed":[116],"using":[117],"soft-attention":[118],"mechanism":[119],"locality":[121],"sensitive":[122],"hashing":[123],"then":[125],"augmented":[126],"into":[127],"model":[129],"training":[131,141],"dataset.":[135,214],"Therefore,":[136],"while":[137],"data,":[142],"also":[144,176],"simultaneously":[145],"exploit":[146],"infuse":[148],"local":[150],"instance-level":[151],"information":[152],"external":[155],"data.":[156],"Using":[157],"approach":[159,181],"have":[161],"shown":[162],"significant":[163,189],"improvements":[164],"over":[165],"baseline":[167],"for":[168,191],"three":[169],"major":[170],"news":[171],"classification":[172],"datasets.":[173],"Experimental":[174],"evaluations":[175],"show":[177,202],"proposed":[180,196],"reduces":[182],"dependency":[183],"labeled":[185],"margin":[190],"comparable":[192],"performance.":[193],"With":[194],"our":[195],"cross":[197],"procedure":[200],"one":[204],"achieve":[206],"competitive/better":[207],"than":[209]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}