{"id":"https://openalex.org/W3005727230","doi":"https://doi.org/10.1109/access.2020.2972611","title":"Neural Langevin Dynamical Sampling","display_name":"Neural Langevin Dynamical Sampling","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3005727230","doi":"https://doi.org/10.1109/access.2020.2972611","mag":"3005727230"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2972611","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08988164.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_indexed_in_scopus":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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08988164.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103153020","display_name":"M. H. Gu","orcid":"https://orcid.org/0000-0002-6206-8401"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"funder","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minghao Gu","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047846625","display_name":"Shiliang Sun","orcid":"https://orcid.org/0000-0001-7069-3752"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"funder","lineage":["https://openalex.org/I116953780"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"funder","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiliang Sun","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China","Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.472,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":17,"citation_normalized_percentile":{"value":0.870651,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":91},"biblio":{"volume":"8","issue":null,"first_page":"31595","last_page":"31605"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9996,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9996,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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":0.9983,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9979,"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/langevin-dynamics","display_name":"Langevin dynamics","score":0.5399253}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.90403795},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65645355},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.55666035},{"id":"https://openalex.org/C2780004032","wikidata":"https://www.wikidata.org/wiki/Q6485978","display_name":"Langevin dynamics","level":2,"score":0.5399253},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.5332911},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.48709202},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4667827},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.44788972},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42760095},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.42364734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39346743},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37033924},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24511608},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23198256},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2972611","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08988164.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_indexed_in_scopus":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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":false,"landing_page_url":"https://doaj.org/article/400701d3d8314bfeac3c007fab567422","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":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2972611","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08988164.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_indexed_in_scopus":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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61673179"}],"datasets":[],"versions":[],"referenced_works_count":27,"referenced_works":["https://openalex.org/W1545319692","https://openalex.org/W1555683961","https://openalex.org/W1604200813","https://openalex.org/W1624558029","https://openalex.org/W1870889549","https://openalex.org/W195465510","https://openalex.org/W1985093013","https://openalex.org/W2059448777","https://openalex.org/W2088538739","https://openalex.org/W2102150307","https://openalex.org/W2138309709","https://openalex.org/W2141436719","https://openalex.org/W2506672536","https://openalex.org/W2521403710","https://openalex.org/W2533385252","https://openalex.org/W2773666931","https://openalex.org/W2889289580","https://openalex.org/W2953989585","https://openalex.org/W2963037621","https://openalex.org/W2963977107","https://openalex.org/W2967519484","https://openalex.org/W3120740533","https://openalex.org/W3125841640","https://openalex.org/W32980360","https://openalex.org/W4242841269","https://openalex.org/W4245795438","https://openalex.org/W788570312"],"related_works":["https://openalex.org/W769381313","https://openalex.org/W4300460000","https://openalex.org/W4294024277","https://openalex.org/W3132384579","https://openalex.org/W3047994252","https://openalex.org/W2963950756","https://openalex.org/W2736517747","https://openalex.org/W2636929808","https://openalex.org/W2592308920","https://openalex.org/W2142819099"],"abstract_inverted_index":{"Sampling":[0],"technique":[1],"is":[2,21,28,143],"one":[3],"of":[4,24,34,46,77,80,87,117,122,132],"the":[5,32,51,66,78,84,88,101,115,120,148,159],"asymptotically":[6],"unbiased":[7],"estimation":[8],"approaches":[9],"for":[10],"inference":[11,33],"in":[12,31],"Bayesian":[13],"probabilistic":[14,36],"models.":[15,37],"Markov":[16],"chain":[17],"Monte":[18,70],"Carlo":[19,71],"(MCMC)":[20],"a":[22,94,129],"kind":[23],"sampling":[25,91,97],"methods,":[26],"which":[27,48,73],"widely":[29],"used":[30],"complex":[35],"However,":[38],"current":[39],"MCMC":[40,55,96,161],"methods":[41],"can":[42],"incur":[43],"high":[44,85],"autocorrelation":[45,153],"samples,":[47],"means":[49],"that":[50,141],"samples":[52,107],"generated":[53],"by":[54],"samplers":[56],"are":[57],"far":[58],"from":[59,147],"independent.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64],"propose":[65,100],"neural":[67,81],"networks":[68,82],"Langevin":[69,89],"(NNLMC)":[72],"makes":[74],"full":[75],"use":[76],"flexibility":[79],"and":[83,108,135,154,157],"efficiency":[86],"dynamics":[90],"to":[92,105,113,145],"construct":[93],"new":[95,102],"method.":[98],"We":[99,124],"update":[103],"function":[104],"generate":[106],"employ":[109],"appropriate":[110],"loss":[111],"functions":[112],"improve":[114],"performance":[116],"NNLMC":[118,142],"during":[119],"process":[121],"sampling.":[123],"evaluate":[125],"our":[126],"method":[127],"on":[128],"large":[130],"diversity":[131],"challenging":[133],"distributions":[134],"real":[136],"datasets.":[137],"Our":[138],"results":[139],"show":[140],"able":[144],"sample":[146],"target":[149],"distribution":[150],"with":[151],"low":[152],"rapid":[155],"convergence,":[156],"outperforms":[158],"state-of-the-art":[160],"samplers.":[162]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3005727230","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1}],"updated_date":"2025-04-21T03:22:51.074003","created_date":"2020-02-24"}