{"id":"https://openalex.org/W7105681540","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229190","title":"Accelerating Simulation-Based Inference with Variational Autoencoders","display_name":"Accelerating Simulation-Based Inference with Variational Autoencoders","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105681540","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229190"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mayank Nautiyal","orcid":null},"institutions":[{"id":"https://openalex.org/I2800139495","display_name":"Science for Life Laboratory","ror":"https://ror.org/04ev03g22","country_code":"SE","type":"facility","lineage":["https://openalex.org/I2800139495"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Mayank Nautiyal","raw_affiliation_strings":["Uppsala University,Science for Life Laboratory,Sweden"],"affiliations":[{"raw_affiliation_string":"Uppsala University,Science for Life Laboratory,Sweden","institution_ids":["https://openalex.org/I2800139495"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Andrey Shternshis","orcid":null},"institutions":[{"id":"https://openalex.org/I2800139495","display_name":"Science for Life Laboratory","ror":"https://ror.org/04ev03g22","country_code":"SE","type":"facility","lineage":["https://openalex.org/I2800139495"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Andrey Shternshis","raw_affiliation_strings":["Uppsala University,Science for Life Laboratory,Sweden"],"affiliations":[{"raw_affiliation_string":"Uppsala University,Science for Life Laboratory,Sweden","institution_ids":["https://openalex.org/I2800139495"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Andreas Hellander","orcid":null},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Andreas Hellander","raw_affiliation_strings":["Uppsala University,Department of Information Technology,Sweden"],"affiliations":[{"raw_affiliation_string":"Uppsala University,Department of Information Technology,Sweden","institution_ids":["https://openalex.org/I123387679"]}]},{"author_position":"last","author":{"id":null,"display_name":"Prashant Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I2800139495","display_name":"Science for Life Laboratory","ror":"https://ror.org/04ev03g22","country_code":"SE","type":"facility","lineage":["https://openalex.org/I2800139495"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Prashant Singh","raw_affiliation_strings":["Uppsala University,Science for Life Laboratory,Sweden"],"affiliations":[{"raw_affiliation_string":"Uppsala University,Science for Life Laboratory,Sweden","institution_ids":["https://openalex.org/I2800139495"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800139495"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.57575352,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8971999883651733,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8971999883651733,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.031599998474121094,"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":0.025599999353289604,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7081999778747559},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6718999743461609},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5882999897003174},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.49149999022483826},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.44760000705718994},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4361000061035156},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4343000054359436},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42309999465942383},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.40939998626708984}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7081999778747559},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6718999743461609},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.630299985408783},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5882999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5843999981880188},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.49149999022483826},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47519999742507935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.459199994802475},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4361000061035156},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4343000054359436},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.3359000086784363},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.3328999876976013},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3037000000476837},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29679998755455017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2896000146865845},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.257999986410141},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2567000091075897},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W362526619","https://openalex.org/W1516111018","https://openalex.org/W2895152177","https://openalex.org/W3031514878","https://openalex.org/W3081344392","https://openalex.org/W4386965882"],"related_works":[],"abstract_inverted_index":{"We":[0,34,89],"present":[1],"a":[2,60,76],"generative":[3],"modeling":[4],"approach":[5,40,96],"based":[6,55],"on":[7,56,105],"the":[8,46,53,72,91,94],"variational":[9,22],"inference":[10],"framework":[11],"for":[12],"likelihood-free":[13],"simulation-based":[14],"inference.":[15],"The":[16,49],"method":[17],"leverages":[18],"latent":[19],"variables":[20],"within":[21],"autoencoders":[23],"to":[24,97],"efficiently":[25],"estimate":[26],"complex":[27,86,99],"posterior":[28,68,87],"distributions":[29],"arising":[30],"from":[31],"stochastic":[32],"simulations.":[33],"explore":[35],"two":[36],"variations":[37],"of":[38,45,93],"this":[39],"distinguished":[41],"by":[42],"their":[43],"treatment":[44],"prior":[47,54,62],"distribution.":[48],"first":[50],"model":[51,74],"adapts":[52],"observed":[57],"data":[58],"using":[59],"multivariate":[61],"network,":[63],"enhancing":[64],"generalization":[65],"across":[66],"various":[67],"queries.":[69],"In":[70],"contrast,":[71],"second":[73],"utilizes":[75],"standard":[77],"Gaussian":[78],"prior,":[79],"offering":[80],"simplicity":[81],"while":[82,101],"still":[83],"effectively":[84],"capturing":[85],"distributions.":[88],"demonstrate":[90],"ability":[92],"proposed":[95],"approximate":[98],"posteriors":[100],"maintaining":[102],"computational":[103],"efficiency":[104],"well-established":[106],"benchmark":[107],"problems.":[108]},"counts_by_year":[],"updated_date":"2025-11-15T23:13:30.683059","created_date":"2025-11-14T00:00:00"}