{"id":"https://openalex.org/W2611544856","doi":"https://doi.org/10.1109/globalsip.2016.7906057","title":"Diffeomorphism learning via relative entropy constrained optimal transport","display_name":"Diffeomorphism learning via relative entropy constrained optimal transport","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2611544856","doi":"https://doi.org/10.1109/globalsip.2016.7906057","mag":"2611544856"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2016.7906057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2016.7906057","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},"type":"conference-paper","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":"https://openalex.org/A5046582466","display_name":"Todd P. Coleman","orcid":"https://orcid.org/0000-0002-2144-2495"},"institutions":[{"id":"https://openalex.org/I887846188","display_name":"National Research Nuclear University MEPhI","ror":"https://ror.org/04w8z7f34","country_code":"RU","type":"education","lineage":["https://openalex.org/I887846188"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Todd P. Coleman","raw_affiliation_strings":["Dept. of Computer Systems and Technologies, MEPhI (Moscow Engineering Physics Institute), Moscow, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Systems and Technologies, MEPhI (Moscow Engineering Physics Institute), Moscow, Russia","institution_ids":["https://openalex.org/I887846188"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019429894","display_name":"Justin Tantiongloc","orcid":null},"institutions":[{"id":"https://openalex.org/I887846188","display_name":"National Research Nuclear University MEPhI","ror":"https://ror.org/04w8z7f34","country_code":"RU","type":"education","lineage":["https://openalex.org/I887846188"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Justin Tantiongloc","raw_affiliation_strings":["Dept. of Computer Systems and Technologies, MEPhI (Moscow Engineering Physics Institute), Moscow, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Systems and Technologies, MEPhI (Moscow Engineering Physics Institute), Moscow, Russia","institution_ids":["https://openalex.org/I887846188"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061445294","display_name":"Alexis B. Allegra","orcid":"https://orcid.org/0000-0002-9811-764X"},"institutions":[{"id":"https://openalex.org/I887846188","display_name":"National Research Nuclear University MEPhI","ror":"https://ror.org/04w8z7f34","country_code":"RU","type":"education","lineage":["https://openalex.org/I887846188"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Alexis Allegra","raw_affiliation_strings":["Dept. of Computer Systems and Technologies, MEPhI (Moscow Engineering Physics Institute), Moscow, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Systems and Technologies, MEPhI (Moscow Engineering Physics Institute), Moscow, Russia","institution_ids":["https://openalex.org/I887846188"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102840572","display_name":"Diego Mesa","orcid":"https://orcid.org/0000-0002-2251-688X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diego Mesa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023457870","display_name":"Dae Young Kang","orcid":"https://orcid.org/0000-0003-2124-4189"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dae Kang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5044833213","display_name":"Marcela Mendoza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marcela Mendoza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":"1","issue":null,"first_page":"1330","last_page":"1334"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9333999752998352,"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":0.9333999752998352,"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/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9282000064849854,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9045000076293945,"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/diffeomorphism","display_name":"Diffeomorphism","score":0.8894454836845398},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5004253387451172},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.4836253225803375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4811933636665344},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.478963166475296},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.4715576171875},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4694212079048157},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.46282655000686646},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.44784772396087646},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4381636679172516},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4328393340110779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36929821968078613},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36074894666671753},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.17302018404006958},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.140140563249588},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.13039475679397583}],"concepts":[{"id":"https://openalex.org/C47556283","wikidata":"https://www.wikidata.org/wiki/Q1058314","display_name":"Diffeomorphism","level":2,"score":0.8894454836845398},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5004253387451172},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.4836253225803375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4811933636665344},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.478963166475296},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.4715576171875},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4694212079048157},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.46282655000686646},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.44784772396087646},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4381636679172516},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4328393340110779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36929821968078613},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36074894666671753},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.17302018404006958},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.140140563249588},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.13039475679397583},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2016.7906057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2016.7906057","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320333591","display_name":"Multidisciplinary University Research Initiative","ror":null},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1482854963","https://openalex.org/W1503893179","https://openalex.org/W1573041381","https://openalex.org/W1585160083","https://openalex.org/W1616126598","https://openalex.org/W1669177583","https://openalex.org/W1925928173","https://openalex.org/W1928736390","https://openalex.org/W1974776581","https://openalex.org/W1981760129","https://openalex.org/W1985506636","https://openalex.org/W2018159038","https://openalex.org/W2088478798","https://openalex.org/W2124116628","https://openalex.org/W2150062983","https://openalex.org/W2219363793","https://openalex.org/W2575883593","https://openalex.org/W2913535645","https://openalex.org/W4213360791","https://openalex.org/W6732123160","https://openalex.org/W6758920291"],"related_works":["https://openalex.org/W4386794506","https://openalex.org/W2075372083","https://openalex.org/W4293508317","https://openalex.org/W3104281043","https://openalex.org/W2891397405","https://openalex.org/W2462155254","https://openalex.org/W4212817163","https://openalex.org/W3217798761","https://openalex.org/W2105808439","https://openalex.org/W2321429733"],"abstract_inverted_index":{"Performing":[0],"inference":[1],"on":[2],"the":[3,39,83,97],"Lie":[4],"group":[5],"of":[6,8,73],"diffeomorphisms":[7,27],"Euclidean":[9],"space":[10],"has":[11],"many":[12],"applications,":[13],"including":[14],"computer":[15],"vision,":[16],"computational":[17,33],"anatomy,":[18],"and":[19,32,51,76,90],"density":[20,84,103],"estimation.":[21],"Computational":[22],"tools":[23],"to":[24,53,60],"find":[25],"such":[26],"typically":[28],"involve":[29],"dynamical":[30],"systems,":[31],"fluid":[34],"mechanics.":[35],"We":[36,99],"here":[37],"consider":[38],"problem":[40],"where":[41,109],"we":[42,79,110],"are":[43],"given":[44],"IID":[45],"samples":[46,62],"from":[47,63],"a":[48,55,61,64],"distribution":[49,66],"P":[50],"want":[52],"learn":[54,96],"diffeomorphism":[56],"that":[57,81],"transforms":[58],"them":[59],"known":[65],"Q.":[67],"Using":[68],"optimal":[69],"transport":[70],"theory,":[71],"properties":[72],"relative":[74],"entropy,":[75],"convex":[77,92],"optimization,":[78],"demonstrate":[80,100],"when":[82],"for":[85,105],"Q":[86],"is":[87],"log-concave,":[88],"efficient":[89],"scalable":[91],"optimization":[93],"algorithms":[94],"can":[95],"diffeomorphism.":[98],"applications":[101],"in":[102],"estimation":[104],"probabilistic":[106],"sleep":[107],"staging":[108],"improve":[111],"classification":[112],"performance.":[113]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
