{"id":"https://openalex.org/W7138873279","doi":"https://doi.org/10.48550/arxiv.2603.17415","title":"Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration","display_name":"Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7138873279","doi":"https://doi.org/10.48550/arxiv.2603.17415"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.17415","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17415","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.2603.17415","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016753399","display_name":"Ivor Simpson","orcid":"https://orcid.org/0000-0001-5605-6626"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simpson, Ivor J. A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087679173","display_name":"Neill D. F. Campbell","orcid":"https://orcid.org/0000-0003-2130-4903"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Campbell, Neill D. F.","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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.2240000069141388,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.2240000069141388,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.13009999692440033,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.07209999859333038,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.6304000020027161},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6269999742507935},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6014000177383423},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5108000040054321},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5040000081062317},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4156999886035919},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4074999988079071},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.3801000118255615},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.6304000020027161},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6269999742507935},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6014000177383423},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5457000136375427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5376999974250793},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4345000088214874},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4156999886035919},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.391400009393692},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.3801000118255615},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33889999985694885},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.33880001306533813},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C34727166","wikidata":"https://www.wikidata.org/wiki/Q515375","display_name":"Cholesky decomposition","level":3,"score":0.32589998841285706},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3100999891757965},{"id":"https://openalex.org/C51110983","wikidata":"https://www.wikidata.org/wiki/Q16503490","display_name":"Overconfidence effect","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.2969000041484833},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.2937999963760376},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2648000121116638},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.26089999079704285}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.17415","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17415","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.2603.17415","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17415","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":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.445957750082016}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Image":[0],"registration":[1,143],"is":[2,149],"an":[3],"ill-posed":[4],"dense":[5,59,141],"vision":[6],"task,":[7],"where":[8],"multiple":[9],"solutions":[10],"achieve":[11],"similar":[12],"loss":[13],"values,":[14],"motivating":[15],"probabilistic":[16],"inference.":[17],"Variational":[18],"inference":[19,73],"has":[20],"previously":[21],"been":[22],"employed":[23],"to":[24,37],"capture":[25],"these":[26],"distributions,":[27,190],"however":[28],"restrictive":[29],"assumptions":[30],"about":[31],"the":[32,51,90,107,134,183],"posterior":[33,189],"form":[34],"can":[35],"lead":[36],"poor":[38],"characterisation,":[39],"overconfidence":[40],"and":[41,70,113,193],"low-quality":[42],"samples.":[43,87],"More":[44],"flexible":[45],"posteriors":[46],"are":[47,164],"typically":[48],"bottlenecked":[49],"by":[50,171],"complexity":[52],"of":[53,82,92,109,136,144],"high-dimensional":[54,103,152],"covariance":[55,104,112],"matrices":[56],"required":[57],"for":[58],"3D":[60,140],"image":[61,142],"registration.":[62],"In":[63],"this":[64,137],"work,":[65],"we":[66,180],"present":[67],"a":[68,93,100,110,114,150],"memory":[69],"computationally":[71,130],"efficient":[72,194],"method,":[74],"Structured":[75],"SIR,":[76],"that":[77,156,163,182],"enables":[78,123],"expressive,":[79],"multi-modal,":[80],"characterisation":[81],"uncertainty":[83,161,195],"with":[84,99],"high":[85],"quality":[86],"We":[88,132,154],"propose":[89],"use":[91],"Sampled":[94],"Importance":[95],"Resampling":[96],"(SIR)":[97],"algorithm":[98],"novel":[101],"memory-efficient":[102],"parameterisation":[105],"as":[106],"sum":[108],"low-rank":[111],"sparse,":[115],"spatially":[116],"structured":[117,187],"Cholesky":[118],"precision":[119],"factor.":[120],"This":[121],"structure":[122],"capturing":[124],"complex":[125],"spatial":[126],"correlations":[127],"while":[128],"remaining":[129],"tractable.":[131],"evaluate":[133],"efficacy":[135],"approach":[138],"in":[139],"brain":[145],"MRI":[146],"data,":[147],"which":[148],"very":[151],"problem.":[153],"demonstrate":[155],"our":[157],"proposed":[158],"method":[159],"produces":[160],"estimates":[162],"significantly":[165],"better":[166,177],"calibrated":[167],"than":[168],"those":[169],"produced":[170],"variational":[172],"methods,":[173],"achieving":[174],"equivalent":[175],"or":[176],"accuracy.":[178],"Crucially,":[179],"show":[181],"model":[184],"yields":[185],"highly":[186],"multi-modal":[188],"enable":[191],"effective":[192],"quantification.":[196]},"counts_by_year":[],"updated_date":"2026-07-02T06:12:58.138171","created_date":"2026-03-20T00:00:00"}