{"id":"https://openalex.org/W4391889550","doi":"https://doi.org/10.1109/comsnets59351.2024.10427073","title":"Generative Learning with Qopula Circuits","display_name":"Generative Learning with Qopula Circuits","publication_year":2024,"publication_date":"2024-01-03","ids":{"openalex":"https://openalex.org/W4391889550","doi":"https://doi.org/10.1109/comsnets59351.2024.10427073"},"language":"en","primary_location":{"id":"doi:10.1109/comsnets59351.2024.10427073","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/comsnets59351.2024.10427073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 16th International Conference on COMmunication Systems &amp; NETworkS (COMSNETS)","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/A5093944000","display_name":"Amey Bhatuse","orcid":null},"institutions":[{"id":"https://openalex.org/I55215948","display_name":"Tata Consultancy Services (India)","ror":"https://ror.org/01b9n8m42","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210086519","https://openalex.org/I55215948"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Amey Bhatuse","raw_affiliation_strings":["Veermata Jijabai Technological Institute,CE &#x0026; IT Department,India","TCS Research, Tata Consultancy Services, India"],"affiliations":[{"raw_affiliation_string":"Veermata Jijabai Technological Institute,CE &#x0026; IT Department,India","institution_ids":[]},{"raw_affiliation_string":"TCS Research, Tata Consultancy Services, India","institution_ids":["https://openalex.org/I55215948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074061540","display_name":"Ankit Khandelwal","orcid":"https://orcid.org/0000-0003-0020-6546"},"institutions":[{"id":"https://openalex.org/I55215948","display_name":"Tata Consultancy Services (India)","ror":"https://ror.org/01b9n8m42","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210086519","https://openalex.org/I55215948"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ankit Khandelwal","raw_affiliation_strings":["Tata Consultancy Services,TCS Research,India","TCS Research, Tata Consultancy Services, India"],"affiliations":[{"raw_affiliation_string":"Tata Consultancy Services,TCS Research,India","institution_ids":["https://openalex.org/I55215948"]},{"raw_affiliation_string":"TCS Research, Tata Consultancy Services, India","institution_ids":["https://openalex.org/I55215948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035666448","display_name":"M Girish Chandra","orcid":"https://orcid.org/0000-0002-1479-0504"},"institutions":[{"id":"https://openalex.org/I55215948","display_name":"Tata Consultancy Services (India)","ror":"https://ror.org/01b9n8m42","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210086519","https://openalex.org/I55215948"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M Girish Chandra","raw_affiliation_strings":["Tata Consultancy Services,TCS Research,India","TCS Research, Tata Consultancy Services, India"],"affiliations":[{"raw_affiliation_string":"Tata Consultancy Services,TCS Research,India","institution_ids":["https://openalex.org/I55215948"]},{"raw_affiliation_string":"TCS Research, Tata Consultancy Services, India","institution_ids":["https://openalex.org/I55215948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5093944000"],"corresponding_institution_ids":["https://openalex.org/I55215948"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01295363,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1012","last_page":"1017"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9861999750137329,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9861999750137329,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9506999850273132,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9453999996185303,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6722115278244019},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5306735634803772},{"id":"https://openalex.org/keywords/electronic-circuit","display_name":"Electronic circuit","score":0.49668341875076294},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40497642755508423},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1699492335319519},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13453087210655212}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6722115278244019},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5306735634803772},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.49668341875076294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40497642755508423},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1699492335319519},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13453087210655212}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comsnets59351.2024.10427073","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/comsnets59351.2024.10427073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 16th International Conference on COMmunication Systems &amp; NETworkS (COMSNETS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1575437604","https://openalex.org/W1991045393","https://openalex.org/W1993440542","https://openalex.org/W2022637272","https://openalex.org/W2037531178","https://openalex.org/W2063545024","https://openalex.org/W2072690700","https://openalex.org/W2083252561","https://openalex.org/W2113987286","https://openalex.org/W2125769353","https://openalex.org/W2266202541","https://openalex.org/W2771527763","https://openalex.org/W2794602324","https://openalex.org/W2797767079","https://openalex.org/W2954728697","https://openalex.org/W2955788542","https://openalex.org/W2970821532","https://openalex.org/W2991888287","https://openalex.org/W3092515858","https://openalex.org/W3099488273","https://openalex.org/W3100993774","https://openalex.org/W4289606390","https://openalex.org/W4308668166","https://openalex.org/W4319596293","https://openalex.org/W4367663152","https://openalex.org/W4388033781","https://openalex.org/W6634450867","https://openalex.org/W6767400126"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2380075625","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"Multivariate":[0],"distribution":[1],"modeling":[2],"is":[3],"a":[4,26,131],"crucial":[5],"process":[6],"employed":[7],"to":[8,41,59,65,88,124,129,145],"capture":[9,146],"dependencies":[10,44],"in":[11,14,45,148],"experimental":[12],"data":[13,91],"areas":[15],"like":[16,93],"physics,":[17],"chemistry,":[18],"finance,":[19],"and":[20,36,79,108,127,143],"climate":[21],"science.":[22],"Copula":[23],"distributions":[24,31],"are":[25],"special":[27],"class":[28],"of":[29,54,75,134],"multivariate":[30],"that":[32,104],"have":[33,37,62,85,105],"uniform":[34],"marginals":[35],"been":[38,63,86],"studied":[39],"extensively":[40],"model":[42,60],"non-trivial":[43],"high-dimensional":[46],"data.":[47,151],"In":[48,111],"the":[49,67,73,117],"last":[50],"decade,":[51],"various":[52,90],"ways":[53],"applying":[55],"machine":[56,77],"learning":[57,78],"techniques":[58],"copulas":[61,126],"proposed":[64],"improve":[66],"pre-existing":[68],"parametric":[69],"models.":[70],"Recently,":[71],"with":[72],"advent":[74],"uniting":[76],"quantum":[80,82,109,122,137],"computing,":[81],"generative":[83,138],"models":[84,103,139],"used":[87],"generate":[89,125],"objects":[92],"images,":[94],"probability":[95],"distributions,":[96],"natural":[97],"language":[98],"data,":[99],"etc,":[100],"using":[101],"hybrid":[102],"both":[106],"classical":[107],"components.":[110],"this":[112],"paper,":[113],"we":[114],"build":[115],"on":[116,120],"recent":[118],"work":[119],"utilizing":[121],"circuits":[123],"aim":[128],"provide":[130],"near-holistic":[132],"study":[133],"how":[135],"different":[136],"can":[140],"be":[141],"implemented":[142],"tuned":[144],"correlations":[147],"real-world":[149],"finance":[150]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
