{"id":"https://openalex.org/W4377861746","doi":"https://doi.org/10.1109/access.2023.3279123","title":"DEMANDE: Density Matrix Neural Density Estimation","display_name":"DEMANDE: Density Matrix Neural Density Estimation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4377861746","doi":"https://doi.org/10.1109/access.2023.3279123"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3279123","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3279123","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10131950.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10131950.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077972491","display_name":"Joseph A. Gallego-Mejia","orcid":"https://orcid.org/0000-0001-8971-4998"},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Joseph A. Gallego-Mejia","raw_affiliation_strings":["Universidad Nacional de Colombia, Bogotá, CO, USA"],"raw_orcid":"https://orcid.org/0000-0001-8971-4998","affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia, Bogotá, CO, USA","institution_ids":["https://openalex.org/I36243813"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080973347","display_name":"Fabio A. Gonz\u00e1lez","orcid":"https://orcid.org/0000-0001-9009-7288"},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":true,"raw_author_name":"Fabio A. Gonz\u00e1lez","raw_affiliation_strings":["Universidad Nacional de Colombia, Bogotá, CO, USA"],"raw_orcid":"https://orcid.org/0000-0001-9009-7288","affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia, Bogotá, CO, USA","institution_ids":["https://openalex.org/I36243813"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5080973347"],"corresponding_institution_ids":["https://openalex.org/I36243813"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.4759,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6748881,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9975000023841858,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9975000023841858,"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":0.9909999966621399,"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":0.9901999831199646,"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/density-estimation","display_name":"Density estimation","score":0.8115662336349487},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.681954562664032},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.6712403893470764},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.6479078531265259},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5912727117538452},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5665864944458008},{"id":"https://openalex.org/keywords/density-matrix","display_name":"Density matrix","score":0.5559094548225403},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5474146604537964},{"id":"https://openalex.org/keywords/multivariate-kernel-density-estimation","display_name":"Multivariate kernel density estimation","score":0.505938708782196},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4591467082500458},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4561554193496704},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.4487518072128296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4455069303512573},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.43703263998031616},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3833928406238556},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.24129310250282288},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23756039142608643},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14578992128372192},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.12932109832763672},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08920985460281372}],"concepts":[{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.8115662336349487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.681954562664032},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.6712403893470764},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.6479078531265259},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5912727117538452},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5665864944458008},{"id":"https://openalex.org/C56911000","wikidata":"https://www.wikidata.org/wiki/Q831774","display_name":"Density matrix","level":3,"score":0.5559094548225403},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5474146604537964},{"id":"https://openalex.org/C84894716","wikidata":"https://www.wikidata.org/wiki/Q6935135","display_name":"Multivariate kernel density estimation","level":5,"score":0.505938708782196},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4591467082500458},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4561554193496704},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.4487518072128296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4455069303512573},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.43703263998031616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3833928406238556},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.24129310250282288},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23756039142608643},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14578992128372192},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.12932109832763672},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08920985460281372},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3279123","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3279123","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10131950.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2b545ccf8fd54cdaae1c6502ba6ba0c7","is_oa":true,"landing_page_url":"https://doaj.org/article/2b545ccf8fd54cdaae1c6502ba6ba0c7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 53062-53078 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3279123","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3279123","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10131950.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4377861746.pdf","grobid_xml":"https://content.openalex.org/works/W4377861746.grobid-xml"},"referenced_works_count":70,"referenced_works":["https://openalex.org/W1496508106","https://openalex.org/W1502922572","https://openalex.org/W1503398984","https://openalex.org/W1516914187","https://openalex.org/W1530235965","https://openalex.org/W1560807810","https://openalex.org/W1583912456","https://openalex.org/W1587559447","https://openalex.org/W1813659000","https://openalex.org/W1901616594","https://openalex.org/W2008056655","https://openalex.org/W2014268383","https://openalex.org/W2049633694","https://openalex.org/W2103936488","https://openalex.org/W2116753372","https://openalex.org/W2118020555","https://openalex.org/W2126843316","https://openalex.org/W2135181320","https://openalex.org/W2136922672","https://openalex.org/W2144902422","https://openalex.org/W2147800946","https://openalex.org/W2165558283","https://openalex.org/W2168227362","https://openalex.org/W2171033594","https://openalex.org/W2549898883","https://openalex.org/W2587284713","https://openalex.org/W2616566795","https://openalex.org/W2750765126","https://openalex.org/W2769182151","https://openalex.org/W2778939515","https://openalex.org/W2919115771","https://openalex.org/W2945406871","https://openalex.org/W2952838738","https://openalex.org/W2953256123","https://openalex.org/W2962906164","https://openalex.org/W2963090522","https://openalex.org/W2963647337","https://openalex.org/W2970265440","https://openalex.org/W2997839189","https://openalex.org/W3017257212","https://openalex.org/W3128950372","https://openalex.org/W3136027411","https://openalex.org/W3144619878","https://openalex.org/W4212863985","https://openalex.org/W4293849739","https://openalex.org/W4297798428","https://openalex.org/W4301259139","https://openalex.org/W4313464628","https://openalex.org/W6610566761","https://openalex.org/W6629804754","https://openalex.org/W6631886430","https://openalex.org/W6635084905","https://openalex.org/W6638304892","https://openalex.org/W6639317949","https://openalex.org/W6675345255","https://openalex.org/W6680498451","https://openalex.org/W6681302627","https://openalex.org/W6681671729","https://openalex.org/W6684452406","https://openalex.org/W6714644935","https://openalex.org/W6729548847","https://openalex.org/W6733471323","https://openalex.org/W6738536549","https://openalex.org/W6744238343","https://openalex.org/W6745384511","https://openalex.org/W6759227737","https://openalex.org/W6762443535","https://openalex.org/W6763486065","https://openalex.org/W6767888642","https://openalex.org/W6848727221"],"related_works":["https://openalex.org/W4241010850","https://openalex.org/W3212687977","https://openalex.org/W2355371556","https://openalex.org/W3123419490","https://openalex.org/W2144201579","https://openalex.org/W1834385407","https://openalex.org/W2026307144","https://openalex.org/W4386285810","https://openalex.org/W2545560175","https://openalex.org/W2776263260"],"abstract_inverted_index":{"Density":[0,68],"estimation":[1,42,59,156,161],"is":[2],"a":[3,16,47,53,82,143],"fundamental":[4],"task":[5],"in":[6,73],"statistics":[7],"and":[8,64,129,147,150,157,176],"machine":[9],"learning":[10,127],"that":[11,27,166],"aims":[12],"to":[13,76,91],"estimate,":[14],"from":[15],"set":[17],"of":[18,24,81,116,136,145],"samples,":[19],"the":[20,25,78,114,117,137,167],"probability":[21,93],"density":[22,41,58,62,155,160],"function":[23],"distribution":[26],"generated":[28],"them.":[29],"There":[30],"are":[31,70,89],"different":[32],"methods":[33,43],"for":[34,56],"addressing":[35],"this":[36,86],"problem":[37],"but":[38],"recently":[39],"deep-neural":[40],"have":[44],"emerged":[45],"as":[46],"powerful":[48],"alternative.":[49],"This":[50],"paper":[51],"presents":[52],"novel":[54],"method":[55,103,139,169],"neural":[57,159],"based":[60],"on":[61,142],"matrices":[63,69],"adaptive":[65],"Fourier":[66],"features.":[67],"commonly":[71],"used":[72,90],"quantum":[74,79,99],"mechanics":[75],"represent":[77],"state":[80],"physical":[83],"system.":[84],"In":[85],"work,":[87],"they":[88],"estimate":[92],"densities":[94],"using":[95,109,131],"an":[96,110],"operation":[97,112],"called":[98],"measurement.":[100],"The":[101,134,163],"proposed":[102,138,168],"can":[104,121],"be":[105,123],"trained":[106,130],"without":[107],"optimization":[108],"averaging":[111],"over":[113],"samples":[115],"training":[118],"dataset.":[119],"It":[120],"also":[122],"integrated":[124],"with":[125,152],"deep":[126],"architectures":[128],"gradient":[132],"descent.":[133],"performance":[135,172],"was":[140],"evaluated":[141],"range":[144],"synthetic":[146],"real":[148],"datasets":[149],"compared":[151],"fast":[153],"kernel":[154],"state-of-the-art":[158],"methods.":[162,181],"results":[164],"demonstrate":[165],"achieves":[170],"competitive":[171],"while":[173],"being":[174],"faster":[175],"more":[177],"efficient":[178],"than":[179],"existing":[180]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}