{"id":"https://openalex.org/W3008246719","doi":null,"title":"Gradient Boosted Flows.","display_name":"Gradient Boosted Flows.","publication_year":2020,"publication_date":"2020-02-27","ids":{"openalex":"https://openalex.org/W3008246719","mag":"3008246719"},"language":"en","primary_location":{"id":"mag:3008246719","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2002.11896","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2002.11896","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053215509","display_name":"Robert A. Giaquinto","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Robert Giaquinto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5014459472","display_name":"Arindam Banerjee","orcid":"https://orcid.org/0000-0002-7856-5699","observed_orcids":["https://orcid.org/0000-0002-7856-5699"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arindam Banerjee","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-7856-5699","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9115999937057495,"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":0.9115999937057495,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.016100000590085983,"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/T10320","display_name":"Neural Networks and Applications","score":0.0142000000923872,"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/normalizing-flows","display_name":"normalizing flows","score":1.0},{"id":"https://openalex.org/keywords/variational-posterior","display_name":"variational posterior","score":0.7229999899864197},{"id":"https://openalex.org/keywords/mixture-models","display_name":"mixture models","score":0.6320000290870667},{"id":"https://openalex.org/keywords/density-estimation","display_name":"density estimation","score":0.7160000205039978},{"id":"https://openalex.org/keywords/variational-autoencoder","display_name":"variational autoencoder","score":0.8420000076293945},{"id":"https://openalex.org/keywords/image-generation","display_name":"image generation","score":0.8069999814033508},{"id":"https://openalex.org/keywords/invertible-transformations","display_name":"invertible transformations","score":0.7429999709129333}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.744744062423706},{"id":"https://openalex.org/C167879884","wikidata":"https://www.wikidata.org/wiki/Q727568","display_name":"Balanced flow","level":2,"score":0.5905827283859253},{"id":"https://openalex.org/C96442724","wikidata":"https://www.wikidata.org/wiki/Q242188","display_name":"Invertible matrix","level":2,"score":0.5583069920539856},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5414472222328186},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5065282583236694},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4993855953216553},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.46891316771507263},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.45309382677078247},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44597601890563965},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43781834840774536},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.4282553791999817},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.4237591028213501},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.37741363048553467},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3577656149864197},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3494861423969269},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29672929644584656},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23574784398078918},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.12245091795921326},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08435392379760742},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08145123720169067},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07681754231452942},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3008246719","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2002.11896","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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}],"best_oa_location":{"id":"mag:3008246719","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2002.11896","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1516111018","https://openalex.org/W1583912456","https://openalex.org/W1866230956","https://openalex.org/W1909320841","https://openalex.org/W1959608418","https://openalex.org/W2024046085","https://openalex.org/W2070493638","https://openalex.org/W2070792261","https://openalex.org/W2104067967","https://openalex.org/W2108263314","https://openalex.org/W2121805075","https://openalex.org/W2135181320","https://openalex.org/W2194321275","https://openalex.org/W2210838531","https://openalex.org/W2409550820","https://openalex.org/W2550354998","https://openalex.org/W2559626456","https://openalex.org/W2560512785","https://openalex.org/W2587284713","https://openalex.org/W2784067649","https://openalex.org/W2791004381","https://openalex.org/W2796426718","https://openalex.org/W2891678505","https://openalex.org/W2895434480","https://openalex.org/W2912618358","https://openalex.org/W2939297204","https://openalex.org/W2962738009","https://openalex.org/W2962947707","https://openalex.org/W2963047245","https://openalex.org/W2963090522","https://openalex.org/W2963135265","https://openalex.org/W2963139417","https://openalex.org/W2964121744","https://openalex.org/W2970898247","https://openalex.org/W6977390765"],"related_works":["https://openalex.org/W3035551016","https://openalex.org/W6913190941","https://openalex.org/W3082602430","https://openalex.org/W7096266597","https://openalex.org/W2963090522","https://openalex.org/W2767227544","https://openalex.org/W7009743671","https://openalex.org/W4379261245","https://openalex.org/W4283316023","https://openalex.org/W7020499040"],"abstract_inverted_index":{"Normalizing":[0],"flows":[1,19],"(NF)":[2],"are":[3,103],"a":[4,12,56,89,94,108,140],"powerful":[5],"framework":[6],"for":[7,132],"approximating":[8],"posteriors.":[9],"By":[10],"mapping":[11],"simple":[13],"base":[14],"density":[15,25,133],"through":[16],"invertible":[17],"transformations,":[18],"provide":[20],"an":[21,49],"exact":[22],"method":[23],"of":[24,79,121,129,145],"evaluation":[26],"and":[27],"sampling.":[28],"The":[29,84],"trend":[30],"in":[31,88],"normalizing":[32],"flow":[33],"literature":[34],"has":[35],"been":[36],"to":[37,43,76,117],"devise":[38],"deeper,":[39,111],"more":[40,101],"complex":[41],"transformations":[42],"achieve":[44],"greater":[45],"flexibility.":[46],"We":[47,125],"propose":[48],"alternative:":[50],"Gradient":[51],"Boosted":[52],"Flows":[53],"(GBF)":[54],"model":[55],"variational":[57,90,141],"posterior":[58,91],"by":[59,65,136],"successively":[60],"adding":[61],"new":[62,71],"NF":[63,72],"components":[64,102],"gradient":[66],"boosting":[67],"so":[68],"that":[69,92,113],"each":[70],"component":[73],"is":[74,93],"fit":[75],"the":[77,80,119,127],"residuals":[78],"previously":[81],"trained":[82],"components.":[83],"GBF":[85,138],"formulation":[86],"results":[87,120],"mixture":[95],"model,":[96],"whose":[97],"flexibility":[98],"increases":[99],"as":[100],"added.":[104],"Moreover,":[105],"GBFs":[106],"offer":[107],"wider,":[109],"not":[110],"approach":[112],"can":[114],"be":[115],"incorporated":[116],"improve":[118],"many":[122],"existing":[123],"NFs.":[124],"demonstrate":[126],"effectiveness":[128],"this":[130],"technique":[131],"estimation":[134],"and,":[135],"coupling":[137],"with":[139],"autoencoder,":[142],"generative":[143],"modeling":[144],"images.":[146]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-03-06T00:00:00"}
