{"id":"https://openalex.org/W7131431151","doi":"https://doi.org/10.48550/arxiv.2602.21185","title":"The Diffusion Duality, Chapter II: $\u03a8$-Samplers and Efficient Curriculum","display_name":"The Diffusion Duality, Chapter II: $\u03a8$-Samplers and Efficient Curriculum","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131431151","doi":"https://doi.org/10.48550/arxiv.2602.21185"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.21185","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/A5126803222","display_name":"Justin Deschenaux","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Deschenaux, Justin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126653781","display_name":"Caglar Gulcehre","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gulcehre, Caglar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101177771","display_name":"Subham Sekhar Sahoo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sahoo, Subham Sekhar","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126803222"],"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":0.2159000039100647,"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":0.2159000039100647,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1005999967455864,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.059300001710653305,"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/perplexity","display_name":"Perplexity","score":0.8245999813079834},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5665000081062317},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5342000126838684},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.47029998898506165},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44780001044273376},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4447999894618988},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.3993000090122223}],"concepts":[{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.8245999813079834},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5665000081062317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5569999814033508},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.47029998898506165},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44519999623298645},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4447999894618988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42660000920295715},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3993000090122223},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.38089999556541443},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37720000743865967},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3434000015258789},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.33640000224113464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3337000012397766},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2992999851703644},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2768000066280365},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.21185","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.2602.21185","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.21185","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.2602.21185","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8189272284507751}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Uniform-state":[0],"discrete":[1,51],"diffusion":[2,23,52,120],"models":[3,24],"excel":[4],"at":[5,83],"few-step":[6],"generation":[7],"and":[8,57,76,89,147,160,162,170],"guidance":[9],"due":[10],"to":[11,14,59,103,152],"their":[12,29],"ability":[13],"self-correct,":[15],"making":[16],"them":[17],"preferred":[18],"over":[19],"autoregressive":[20],"or":[21],"Masked":[22,119],"in":[25],"these":[26,111],"settings.":[27],"However,":[28],"sampling":[30,72,107],"quality":[31],"plateaus":[32],"with":[33,65,105],"ancestral":[34,71],"samplers":[35,49,69],"as":[36],"the":[37,116,122,137],"number":[38],"of":[39,46,125],"steps":[40],"increases.":[41],"We":[42,166],"introduce":[43],"a":[44,133,171],"family":[45],"Predictor-Corrector":[47],"(PC)":[48],"for":[50,136],"that":[53,118],"generalize":[54],"prior":[55],"methods":[56,101],"apply":[58],"arbitrary":[60],"noise":[61],"processes.":[62],"When":[63],"paired":[64],"uniform-state":[66],"diffusion,":[67],"our":[68,99],"outperform":[70],"on":[73,87,93,158],"both":[74],"language":[75,127],"image":[77],"modeling,":[78],"achieving":[79],"lower":[80],"generative":[81],"perplexity":[82,157],"matched":[84],"unigram":[85],"entropy":[86],"OpenWebText":[88,159],"better":[90],"FID/IS":[91],"scores":[92],"CIFAR10.":[94],"Crucially,":[95],"unlike":[96],"conventional":[97],"samplers,":[98],"PC":[100],"continue":[102],"improve":[104],"more":[106],"steps.":[108],"Taken":[109],"together,":[110],"findings":[112],"call":[113],"into":[114],"question":[115],"assumption":[117],"is":[121],"inevitable":[123],"future":[124],"diffusion-based":[126],"modeling.":[128],"Beyond":[129],"sampling,":[130],"we":[131],"develop":[132],"memory-efficient":[134],"curriculum":[135],"Gaussian":[138],"relaxation":[139],"training":[140,143],"phase,":[141],"reducing":[142],"time":[144],"by":[145,149],"25%":[146],"memory":[148],"33%":[150],"compared":[151],"Duo":[153],"while":[154],"maintaining":[155],"comparable":[156],"LM1B":[161],"strong":[163],"downstream":[164],"performance.":[165],"release":[167],"code,":[168],"checkpoints,":[169],"video-tutorial":[172],"on:":[173],"https://s-sahoo.com/duo-ch2":[174]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-26T00:00:00"}
