{"id":"https://openalex.org/W2913199206","doi":"https://doi.org/10.1109/tnnls.2019.2945111","title":"Geometric Matrix Completion With Deep Conditional Random Fields","display_name":"Geometric Matrix Completion With Deep Conditional Random Fields","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2913199206","doi":"https://doi.org/10.1109/tnnls.2019.2945111","mag":"2913199206","pmid":"https://pubmed.ncbi.nlm.nih.gov/31689219"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2019.2945111","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2019.2945111","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1901.10429","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101961269","display_name":"Duc Minh Nguyen","orcid":"https://orcid.org/0000-0002-0367-564X"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]},{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Duc Minh Nguyen","raw_affiliation_strings":["Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussels, Belgium","imec, Leuven, Belgium"],"raw_orcid":"https://orcid.org/0000-0002-0367-564X","affiliations":[{"raw_affiliation_string":"Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussels, Belgium","institution_ids":["https://openalex.org/I13469542"]},{"raw_affiliation_string":"imec, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037795370","display_name":"Robert Calderbank","orcid":"https://orcid.org/0000-0003-2084-9717"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Calderbank","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Duke University, Durham, USA","Duke University#TAB#"],"raw_orcid":"https://orcid.org/0000-0003-2084-9717","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Duke University, Durham, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Duke University#TAB#","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043511500","display_name":"Nikos Deligiannis","orcid":"https://orcid.org/0000-0001-9300-5860"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]},{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Nikos Deligiannis","raw_affiliation_strings":["Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussels, Belgium","imec, Leuven, Belgium"],"raw_orcid":"https://orcid.org/0000-0001-9300-5860","affiliations":[{"raw_affiliation_string":"Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussels, Belgium","institution_ids":["https://openalex.org/I13469542"]},{"raw_affiliation_string":"imec, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"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":0.01481414,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":"9","first_page":"3579","last_page":"3593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9897000193595886,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9897000193595886,"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":0.9886999726295471,"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/T11106","display_name":"Data Management and Algorithms","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/matrix-completion","display_name":"Matrix completion","score":0.7867901921272278},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7291547060012817},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.727767825126648},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6221495270729065},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5069184899330139},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.4908943772315979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4876655340194702},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.46723490953445435},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.46246641874313354},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.44139739871025085},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43055158853530884},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4277764856815338},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42649373412132263},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.41487547755241394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39400649070739746},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39125558733940125},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1432974934577942}],"concepts":[{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.7867901921272278},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7291547060012817},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.727767825126648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6221495270729065},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5069184899330139},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.4908943772315979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4876655340194702},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.46723490953445435},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.46246641874313354},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.44139739871025085},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43055158853530884},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4277764856815338},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42649373412132263},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41487547755241394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39400649070739746},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39125558733940125},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1432974934577942},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/tnnls.2019.2945111","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2019.2945111","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:31689219","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31689219","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:arXiv.org:1901.10429","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1901.10429","pdf_url":"https://arxiv.org/pdf/1901.10429","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":"","raw_type":"text"},{"id":"mag:2913199206","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1901.10429.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.1901.10429","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1901.10429","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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1901.10429","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1901.10429","pdf_url":"https://arxiv.org/pdf/1901.10429","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":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2913199206.pdf","grobid_xml":"https://content.openalex.org/works/W2913199206.grobid-xml"},"referenced_works_count":79,"referenced_works":["https://openalex.org/W27675589","https://openalex.org/W1479822238","https://openalex.org/W1511986666","https://openalex.org/W1587275696","https://openalex.org/W1595128708","https://openalex.org/W1665214252","https://openalex.org/W1720514416","https://openalex.org/W1814721328","https://openalex.org/W1836465849","https://openalex.org/W1855068118","https://openalex.org/W1921958996","https://openalex.org/W1969698720","https://openalex.org/W1992270714","https://openalex.org/W1996283866","https://openalex.org/W2027829212","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2056609785","https://openalex.org/W2095705004","https://openalex.org/W2099712288","https://openalex.org/W2100235918","https://openalex.org/W2106005123","https://openalex.org/W2108919995","https://openalex.org/W2117311203","https://openalex.org/W2124592697","https://openalex.org/W2134332047","https://openalex.org/W2135598826","https://openalex.org/W2139193890","https://openalex.org/W2143701678","https://openalex.org/W2144487656","https://openalex.org/W2145962650","https://openalex.org/W2146682077","https://openalex.org/W2152463966","https://openalex.org/W2158823144","https://openalex.org/W2161236525","https://openalex.org/W2165874743","https://openalex.org/W2165949563","https://openalex.org/W2186878252","https://openalex.org/W2219888463","https://openalex.org/W2471920251","https://openalex.org/W2551968180","https://openalex.org/W2558748708","https://openalex.org/W2611328865","https://openalex.org/W2624407581","https://openalex.org/W2743992351","https://openalex.org/W2784226479","https://openalex.org/W2796303740","https://openalex.org/W2800011138","https://openalex.org/W2893671662","https://openalex.org/W2962746029","https://openalex.org/W2963043672","https://openalex.org/W2963052833","https://openalex.org/W2963354044","https://openalex.org/W2963389687","https://openalex.org/W2963739249","https://openalex.org/W2963979542","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2964273061","https://openalex.org/W3102895136","https://openalex.org/W4249267926","https://openalex.org/W6631190155","https://openalex.org/W6631990664","https://openalex.org/W6635012147","https://openalex.org/W6637242042","https://openalex.org/W6638551751","https://openalex.org/W6638667902","https://openalex.org/W6648444898","https://openalex.org/W6674330103","https://openalex.org/W6680779052","https://openalex.org/W6681456887","https://openalex.org/W6682238262","https://openalex.org/W6684578312","https://openalex.org/W6686968995","https://openalex.org/W6714585510","https://openalex.org/W6726873649","https://openalex.org/W6737558694","https://openalex.org/W6742379965","https://openalex.org/W7052067543"],"related_works":["https://openalex.org/W2985663642","https://openalex.org/W2963043672","https://openalex.org/W3183485661","https://openalex.org/W2796102628","https://openalex.org/W2068176821","https://openalex.org/W2275207081","https://openalex.org/W2998244479","https://openalex.org/W2612914723","https://openalex.org/W2953096735","https://openalex.org/W2912708663","https://openalex.org/W2468625129","https://openalex.org/W3170846394","https://openalex.org/W2605907621","https://openalex.org/W2396730968","https://openalex.org/W2900042804","https://openalex.org/W1843494895","https://openalex.org/W2346211548","https://openalex.org/W3167196392","https://openalex.org/W2557135006","https://openalex.org/W3210457622"],"abstract_inverted_index":{"The":[0,21,191],"problem":[1,157],"of":[2,10,124,225,234],"completing":[3],"high-dimensional":[4],"matrices":[5],"from":[6,44],"a":[7,29,32,61,80,103,142,154,159,168,170,177,219],"limited":[8],"set":[9],"observations":[11,87,126],"arises":[12],"in":[13,55,158,214],"many":[14],"big":[15],"data":[16,246],"applications,":[17],"especially":[18],"recommender":[19],"systems.":[20],"existing":[22,130],"matrix":[23,37,151,199],"completion":[24,38,152],"models":[25,40,50,99,242],"generally":[26],"follow":[27],"either":[28],"memory-":[30],"or":[31,88],"model-based":[33],"approach,":[34],"whereas":[35],"geometric":[36,49],"(GMC)":[39],"combine":[41],"the":[42,66,69,85,122,182,187,196,202,207,223,231,235],"best":[43],"both":[45],"approaches.":[46],"Existing":[47],"deep-learning-based":[48],"yield":[51],"good":[52],"performance,":[53],"but,":[54],"order":[56],"to":[57,112,134,221,239,253],"operate,":[58],"they":[59],"require":[60,102],"fixed":[62,104],"structure":[63,105],"graph":[64,74,106],"capturing":[65],"relationships":[67],"among":[68,198],"users":[70],"and":[71,121,174,205,248],"items.":[72],"This":[73],"is":[75,119,165,212],"typically":[76],"constructed":[77],"by":[78,89,167,185],"evaluating":[79],"pre-defined":[81],"similarity":[82],"metric":[83],"on":[84,109,243],"available":[86,120,125],"using":[90],"side":[91,117],"information,":[92],"e.g.,":[93],"user":[94],"profiles.":[95],"In":[96,137],"contrast,":[97],"Markov-random-fields-based":[98],"do":[100],"not":[101],"but":[107],"rely":[108],"handcrafted":[110],"features":[111],"make":[113],"predictions.":[114],"When":[115],"no":[116],"information":[118],"number":[123],"becomes":[127],"very":[128],"low,":[129],"solutions":[131],"are":[132],"pushed":[133],"their":[135],"limits.":[136],"this":[138],"article,":[139],"we":[140,175],"propose":[141,176],"GMC":[143],"approach":[144],"that":[145,180],"addresses":[146],"these":[147],"challenges.":[148],"We":[149],"consider":[150],"as":[153],"structured":[155],"prediction":[156],"conditional":[160],"random":[161],"field":[162],"(CRF),":[163],"which":[164],"characterized":[166],"maximum":[169],"posteriori":[171],"(MAP)":[172],"inference,":[173],"deep":[178],"model":[179,193,237],"predicts":[181],"missing":[183],"entries":[184],"solving":[186],"MAP":[188],"inference":[189,208],"problem.":[190,209],"proposed":[192,236],"simultaneously":[194],"learns":[195],"similarities":[197],"entries,":[200],"computes":[201],"CRF":[203],"potentials,":[204],"solves":[206],"Its":[210],"training":[211],"performed":[213],"an":[215],"end-to-end":[216],"manner,":[217],"with":[218,255],"method":[220],"supervise":[222],"learning":[224],"entry":[226],"similarities.":[227],"Comprehensive":[228],"experiments":[229],"demonstrate":[230],"superior":[232,251],"performance":[233],"compared":[238],"various":[240],"state-of-the-art":[241],"popular":[244],"benchmark":[245],"sets":[247],"underline":[249],"its":[250],"capacity":[252],"deal":[254],"highly":[256],"incomplete":[257],"matrices.":[258]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
