{"id":"https://openalex.org/W7131394490","doi":"https://doi.org/10.48550/arxiv.2602.19600","title":"Manifold-Aligned Generative Transport","display_name":"Manifold-Aligned Generative Transport","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131394490","doi":"https://doi.org/10.48550/arxiv.2602.19600"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.19600","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19600","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.19600","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126745044","display_name":"Xinyu Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126700357","display_name":"Xiaotong Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Xiaotong","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":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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8913000226020813,"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":0.8913000226020813,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.01769999973475933,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.0142000000923872,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6452000141143799},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5504000186920166},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5315999984741211},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.49129998683929443},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4537000060081482},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.44690001010894775},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44200000166893005},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4036000072956085},{"id":"https://openalex.org/keywords/probability-mass-function","display_name":"Probability mass function","score":0.3953000009059906},{"id":"https://openalex.org/keywords/diffusion-map","display_name":"Diffusion map","score":0.3758000135421753}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6452000141143799},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5504000186920166},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5315999984741211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4916999936103821},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4537000060081482},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.44690001010894775},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4323999881744385},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42590001225471497},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4036000072956085},{"id":"https://openalex.org/C197096303","wikidata":"https://www.wikidata.org/wiki/Q869887","display_name":"Probability mass function","level":3,"score":0.3953000009059906},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.3758000135421753},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36910000443458557},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34049999713897705},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C30732413","wikidata":"https://www.wikidata.org/wiki/Q17092636","display_name":"Intrinsic dimension","level":3,"score":0.322299987077713},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.31369999051094055},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2953000068664551},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.29159998893737793},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.27059999108314514},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.25940001010894775},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.19600","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19600","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.19600","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19600","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-dimensional":[0],"generative":[1,175],"modeling":[2],"is":[3,94,105],"fundamentally":[4],"a":[5,12,75,80,85,97,116,128,134],"manifold-learning":[6],"problem:":[7],"real":[8],"data":[9,33,91],"concentrate":[10],"near":[11,31,140],"low-dimensional":[13,86],"structure":[14,44],"embedded":[15],"in":[16,58,133],"the":[17,32,90,103,141,152],"ambient":[18],"space.":[19,92],"Effective":[20],"generators":[21],"must":[22],"therefore":[23],"balance":[24],"support":[25],"fidelity":[26,180],"--":[27,35],"placing":[28],"probability":[29,139],"mass":[30],"manifold":[34,153,182],"with":[36,123,149],"sampling":[37,190],"efficiency.":[38],"Diffusion":[39],"models":[40],"often":[41],"capture":[42],"near-manifold":[43],"but":[45,61],"require":[46],"many":[47],"iterative":[48],"denoising":[49],"steps":[50],"and":[51,66,107,144,171,177,181,186],"can":[52],"leak":[53],"off-support;":[54],"normalizing":[55],"flows":[56],"sample":[57],"one":[59],"pass":[60],"are":[62],"limited":[63],"by":[64],"invertibility":[65],"dimension":[67],"preservation.":[68],"We":[69,110,163],"propose":[70],"MAGT":[71,131],"(Manifold-Aligned":[72],"Generative":[73],"Transport),":[74],"flow-like":[76],"generator":[77],"that":[78],"learns":[79],"one-shot,":[81],"manifold-aligned":[82],"transport":[83],"from":[84],"base":[87],"distribution":[88],"to":[89,151,174],"Training":[93],"performed":[95],"at":[96],"fixed":[98],"Gaussian":[99],"smoothing":[100,169],"level,":[101],"where":[102],"score":[104,114],"well-defined":[106],"numerically":[108],"stable.":[109],"approximate":[111],"this":[112],"fixed-level":[113],"using":[115],"finite":[117],"set":[118],"of":[119],"latent":[120],"anchor":[121],"points":[122],"self-normalized":[124],"importance":[125],"sampling,":[126],"yielding":[127],"tractable":[129],"objective.":[130],"samples":[132],"single":[135],"forward":[136],"pass,":[137],"concentrates":[138],"learned":[142],"support,":[143],"induces":[145],"an":[146],"intrinsic":[147],"density":[148],"respect":[150],"volume":[154],"measure,":[155],"enabling":[156],"principled":[157],"likelihood":[158],"evaluation":[159],"for":[160],"generated":[161],"samples.":[162],"establish":[164],"finite-sample":[165],"Wasserstein":[166],"bounds":[167],"linking":[168],"level":[170],"score-approximation":[172],"accuracy":[173],"fidelity,":[176],"empirically":[178],"improve":[179],"concentration":[183],"across":[184],"synthetic":[185],"benchmark":[187],"datasets":[188],"while":[189],"substantially":[191],"faster":[192],"than":[193],"diffusion":[194],"models.":[195]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-02-26T00:00:00"}