{"id":"https://openalex.org/W4409983179","doi":"https://doi.org/10.1137/1.9781611978520.1","title":"Automated Learning of Semantic Embedding Representations for Diffusion Models","display_name":"Automated Learning of Semantic Embedding Representations for Diffusion Models","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409983179","doi":"https://doi.org/10.1137/1.9781611978520.1"},"language":"en","primary_location":{"id":"doi:10.1137/1.9781611978520.1","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611978520.1","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.05732","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019705983","display_name":"Limai Jiang","orcid":"https://orcid.org/0000-0001-6321-7571"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Limai Jiang","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences","University of Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210145761"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090194899","display_name":"Yunpeng Cai","orcid":"https://orcid.org/0000-0001-8797-4243"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunpeng Cai","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210145761"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019705983"],"corresponding_institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":4.9041,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.9279732,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14351","display_name":"Statistical and Computational Modeling","score":0.6510999798774719,"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"}},"topics":[{"id":"https://openalex.org/T14351","display_name":"Statistical and Computational Modeling","score":0.6510999798774719,"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/embedding","display_name":"Embedding","score":0.6387201547622681},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5216298699378967},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5204025506973267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49545443058013916},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.45749199390411377},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3888811469078064},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09377577900886536},{"id":"https://openalex.org/keywords/quantum-mechanics","display_name":"Quantum mechanics","score":0.04531940817832947}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6387201547622681},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5216298699378967},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5204025506973267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49545443058013916},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.45749199390411377},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3888811469078064},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09377577900886536},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.04531940817832947}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1137/1.9781611978520.1","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611978520.1","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"},{"id":"pmh:oai:arXiv.org:2505.05732","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.05732","pdf_url":"https://arxiv.org/pdf/2505.05732","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2505.05732","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.05732","pdf_url":"https://arxiv.org/pdf/2505.05732","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2931662336","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Generative":[0],"models":[1,14],"capture":[2],"the":[3,40,61,70,74,91,101],"true":[4],"distribution":[5],"of":[6,43,93,104],"data,":[7],"yielding":[8],"semantically":[9],"rich":[10],"representations.":[11],"Denoising":[12],"diffusion":[13,67,76],"(DDMs)":[15],"exhibit":[16],"superior":[17],"generative":[18,149],"capabilities,":[19],"though":[20],"efficient":[21],"representation":[22,41,127,137],"learning":[23,92,128,158],"for":[24,148,155],"them":[25],"are":[26,112,144],"lacking.":[27],"In":[28],"this":[29,108],"work,":[30],"we":[31],"employ":[32],"a":[33],"multi-level":[34],"denoising":[35,62],"autoencoder":[36],"framework":[37],"to":[38,56],"expand":[39],"capacity":[42],"DDMs,":[44],"which":[45],"introduces":[46],"sequentially":[47],"consistent":[48],"Diffusion":[49],"Transformers":[50],"and":[51],"an":[52],"additional":[53],"timestep-dependent":[54],"encoder":[55],"acquire":[57],"embedding":[58],"representations":[59],"on":[60,73,114],"Markov":[63],"chain":[64],"through":[65,107],"self-conditional":[66],"learning.":[68],"Intuitively,":[69],"encoder,":[71],"conditioned":[72],"entire":[75],"process,":[77],"compresses":[78],"high-dimensional":[79],"data":[80],"into":[81],"directional":[82],"vectors":[83],"in":[84,130],"latent":[85],"under":[86],"different":[87],"noise":[88],"levels,":[89],"facilitating":[90],"image":[94],"embeddings":[95,105,121],"across":[96],"all":[97],"timesteps.":[98],"To":[99],"verify":[100],"semantic":[102,136],"adequacy":[103],"generated":[106],"approach,":[109],"extensive":[110],"experiments":[111],"conducted":[113],"various":[115],"datasets,":[116],"demonstrating":[117],"that":[118,142],"optimally":[119],"learned":[120],"by":[122],"DDMs":[123,143],"surpass":[124],"state-of-the-art":[125],"self-supervised":[126],"methods":[129],"most":[131],"cases,":[132],"achieving":[133],"remarkable":[134],"discriminative":[135],"quality.":[138],"Our":[139],"work":[140],"justifies":[141],"not":[145],"only":[146],"suitable":[147],"tasks,":[150],"but":[151],"also":[152],"potentially":[153],"advantageous":[154],"general-purpose":[156],"deep":[157],"applications.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}