{"id":"https://openalex.org/W7128612166","doi":"https://doi.org/10.48550/arxiv.2602.09983","title":"Coupled Inference in Diffusion Models for Semantic Decomposition","display_name":"Coupled Inference in Diffusion Models for Semantic Decomposition","publication_year":2026,"publication_date":"2026-02-10","ids":{"openalex":"https://openalex.org/W7128612166","doi":"https://doi.org/10.48550/arxiv.2602.09983"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.09983","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034093897","display_name":"Calvin Yeung","orcid":"https://orcid.org/0009-0008-3326-8931"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yeung, Calvin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125666562","display_name":"Ali Zakeri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zakeri, Ali","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035947458","display_name":"Zhuowen Zou","orcid":"https://orcid.org/0000-0001-9057-8815"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Zhuowen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125631397","display_name":"Mohsen Imani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Imani, Mohsen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5034093897"],"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":0.46810001134872437,"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":0.46810001134872437,"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/T11094","display_name":"Face Recognition and Perception","score":0.1729000061750412,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1396999955177307,"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/inference","display_name":"Inference","score":0.7085000276565552},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.6252999901771545},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.45509999990463257},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.44760000705718994},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.43299999833106995},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.40610000491142273},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.39340001344680786},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.35429999232292175}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7085000276565552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523000001907349},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.6252999901771545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4569999873638153},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44110000133514404},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37130001187324524},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.33239999413490295},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.33070001006126404},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2858999967575073},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2784999907016754},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.26010000705718994},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.09983","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.09983","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.09983","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.09983","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Many":[0],"visual":[1],"scenes":[2],"can":[3,45],"be":[4,46],"described":[5],"as":[6,48,54,98],"compositions":[7],"of":[8,116,136,150,167],"latent":[9],"factors.":[10],"Effective":[11],"recognition,":[12],"reasoning,":[13],"and":[14,73,102],"editing":[15],"often":[16],"require":[17],"not":[18],"only":[19],"forming":[20],"such":[21,61],"compositional":[22],"representations,":[23],"but":[24],"also":[25,125],"solving":[26],"the":[27,39,104,114,121,134],"decomposition":[28,59,86,97,170],"problem.":[29],"One":[30],"popular":[31],"choice":[32],"for":[33,84],"constructing":[34],"these":[35,78],"representations":[36],"is":[37],"through":[38],"binding":[40],"operation.":[41],"Resonator":[42],"networks,":[43,51],"which":[44],"understood":[47],"coupled":[49,88,158],"Hopfield":[50,71],"were":[52],"proposed":[53],"a":[55,82,108,127,147,165],"way":[56],"to":[57,119],"perform":[58],"on":[60],"bound":[62,122],"representations.":[63],"Recent":[64],"works":[65],"have":[66],"shown":[67],"notable":[68],"similarities":[69],"between":[70],"networks":[72,145,163],"diffusion":[74,91,105],"models.":[75,92],"Motivated":[76],"by":[77],"observations,":[79],"we":[80,140,154],"introduce":[81,126],"framework":[83,160],"semantic":[85,96,169],"using":[87,107],"inference":[89,159],"in":[90],"Our":[93],"method":[94],"frames":[95],"an":[99],"inverse":[100],"problem":[101],"couples":[103],"processes":[106],"reconstruction-driven":[109],"guidance":[110],"term":[111],"that":[112,132,142,156],"encourages":[113],"composition":[115],"factor":[117],"estimates":[118],"match":[120],"vector.":[123],"We":[124],"novel":[128],"iterative":[129],"sampling":[130],"scheme":[131],"improves":[133],"performance":[135],"our":[137,151,157],"model.":[138],"Finally,":[139],"show":[141],"attention-based":[143],"resonator":[144,162],"are":[146],"special":[148],"case":[149],"framework.":[152],"Empirically,":[153],"demonstrate":[155],"outperforms":[161],"across":[164],"range":[166],"synthetic":[168],"tasks.":[171]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-12T00:00:00"}