{"id":"https://openalex.org/W4411624613","doi":"https://doi.org/10.1145/3703323.3703339","title":"Application Specific Compression of Deep Learning Models","display_name":"Application Specific Compression of Deep Learning Models","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4411624613","doi":"https://doi.org/10.1145/3703323.3703339"},"language":"en","primary_location":{"id":"doi:10.1145/3703323.3703339","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703339","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3703323.3703339","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 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3703323.3703339","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103014511","display_name":"Rohit Raj","orcid":"https://orcid.org/0009-0004-6002-1767"},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rohit Raj Rai","raw_affiliation_strings":["Computer Science and Engineering, IIT Guwahati, Guwahati, Assam, IN"],"raw_orcid":"https://orcid.org/0009-0004-6002-1767","affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, IIT Guwahati, Guwahati, Assam, IN","institution_ids":["https://openalex.org/I1317621060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112651615","display_name":"Angana Borah","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Angana Borah","raw_affiliation_strings":["University of Michigan Ann Arbor, Ann Arbor, Michigan, US"],"raw_orcid":"https://orcid.org/0009-0002-7736-274X","affiliations":[{"raw_affiliation_string":"University of Michigan Ann Arbor, Ann Arbor, Michigan, US","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000346770","display_name":"Amit Awekar","orcid":"https://orcid.org/0000-0003-1559-8247"},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amit Awekar","raw_affiliation_strings":["IIT Guwahati, Guwahati, Assam, IN"],"raw_orcid":"https://orcid.org/0000-0003-1559-8247","affiliations":[{"raw_affiliation_string":"IIT Guwahati, Guwahati, Assam, IN","institution_ids":["https://openalex.org/I1317621060"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26762003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"102","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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":0.9994000196456909,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9930999875068665,"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/T13650","display_name":"Computational Physics and Python Applications","score":0.9912999868392944,"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":0.6892423033714294},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.490173876285553},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4428701400756836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4337509870529175},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4183503985404968},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06377118825912476}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6892423033714294},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.490173876285553},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4428701400756836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4337509870529175},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4183503985404968},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06377118825912476},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3703323.3703339","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703339","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3703323.3703339","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 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3703323.3703339","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3703323.3703339","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3703323.3703339","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 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411624613.pdf","grobid_xml":"https://content.openalex.org/works/W4411624613.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W1840435438","https://openalex.org/W1899794420","https://openalex.org/W2946794439","https://openalex.org/W2963748441","https://openalex.org/W2964217848","https://openalex.org/W3035038672","https://openalex.org/W3105966348","https://openalex.org/W3173374050","https://openalex.org/W4205952419","https://openalex.org/W4220785642","https://openalex.org/W4404781961","https://openalex.org/W4404782058"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"Large":[0],"Deep":[1,13,71],"Learning":[2,14,72],"models":[3,32,118],"are":[4,33,75],"compressed":[5,31,48,143],"and":[6,65,127,141],"deployed":[7],"for":[8,54,78,106,119],"specific":[9],"applications.":[10],"However,":[11],"current":[12],"model":[15,42,49,73,138],"compression":[16,43,139],"methods":[17,140],"do":[18,97],"not":[19,98],"utilize":[20],"the":[21,24,30,41,55,69,79,91,94,103,107,114],"information":[22],"about":[23],"target":[25,56,81],"application.":[26,57,82,109],"As":[27],"a":[28,47],"result,":[29],"application":[34],"agnostic.":[35],"Our":[36,58],"goal":[37],"is":[38,88],"to":[39,45,89,101],"customize":[40],"process":[44],"create":[46],"that":[50,74,96,132],"will":[51],"perform":[52],"better":[53,135],"method,":[59],"Application":[60],"Specific":[61],"Compression":[62],"(ASC),":[63],"identifies":[64],"prunes":[66],"components":[67],"of":[68,85,93,117],"large":[70],"redundant":[76],"specifically":[77],"given":[80,108],"The":[83],"intuition":[84],"our":[86],"work":[87],"prune":[90],"parts":[92],"network":[95],"contribute":[99],"significantly":[100],"updating":[102],"data":[104],"representation":[105],"We":[110,130],"have":[111],"experimented":[112],"with":[113],"BERT":[115],"family":[116],"three":[120],"applications:":[121],"Extractive":[122],"QA,":[123],"Natural":[124],"Language":[125],"Inference,":[126],"Paraphrase":[128],"Identification.":[129],"observe":[131],"ASC":[133],"performs":[134],"than":[136],"existing":[137],"off-the-shelf":[142],"models.":[144]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}