{"id":"https://openalex.org/W3004985245","doi":"https://doi.org/10.5555/2567709.2567749","title":"Training energy-based models for time-series imputation","display_name":"Training energy-based models for time-series imputation","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W3004985245","doi":"https://doi.org/10.5555/2567709.2567749","mag":"3004985245"},"language":"en","primary_location":{"id":"mag:3004985245","is_oa":false,"landing_page_url":"https://dl.acm.org/doi/10.5555/2567709.2567749","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null},"type":"article","indexed_in":[],"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/A5061838428","display_name":"BrakelPhil\u00e9mon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"BrakelPhil\u00e9mon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047660961","display_name":"StroobandtDirk","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"StroobandtDirk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5034208900","display_name":"SchrauwenBenjamin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"SchrauwenBenjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4603,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.87392174,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.98580002784729,"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/T10320","display_name":"Neural Networks and Applications","score":0.98580002784729,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9657999873161316,"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/imputation","display_name":"Imputation (statistics)","score":0.6320850253105164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6182169318199158},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5493011474609375},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5222183465957642},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.501317024230957},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4802146852016449},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4523606300354004},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3767872750759125},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.29806679487228394}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6320850253105164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6182169318199158},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5493011474609375},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5222183465957642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.501317024230957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4802146852016449},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4523606300354004},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3767872750759125},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.29806679487228394},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3004985245","is_oa":false,"landing_page_url":"https://dl.acm.org/doi/10.5555/2567709.2567749","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8799999952316284,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2340168176","https://openalex.org/W2210664258","https://openalex.org/W2967577614","https://openalex.org/W168476679","https://openalex.org/W3013362261","https://openalex.org/W2902208067","https://openalex.org/W2996820843","https://openalex.org/W3121341047","https://openalex.org/W3205421793","https://openalex.org/W2383606712","https://openalex.org/W1971186285","https://openalex.org/W2013097030","https://openalex.org/W2897331688","https://openalex.org/W2956776422","https://openalex.org/W2563201119","https://openalex.org/W2380165336","https://openalex.org/W1903965216","https://openalex.org/W3144505278","https://openalex.org/W2183358685","https://openalex.org/W2982044626"],"abstract_inverted_index":{"Imputing":[0],"missing":[1],"values":[2],"in":[3],"high":[4],"dimensional":[5],"time-series":[6],"is":[7],"a":[8,14],"difficult":[9],"problem.":[10],"This":[11],"paper":[12],"presents":[13],"strategy":[15],"for":[16,21],"training":[17],"energy-based":[18],"graphical":[19],"models":[20],"imputation":[22],"directly,":[23],"bypassing":[24],"difficul...":[25]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2025-10-10T17:16:08.811792","created_date":"2025-10-10T00:00:00"}
