{"id":"https://openalex.org/W7151509179","doi":"https://doi.org/10.1109/icmla66185.2025.00194","title":"iDiffODE: Learning Continuous-Time Latent Dynamics for Generative Spatiotemporal Modeling","display_name":"iDiffODE: Learning Continuous-Time Latent Dynamics for Generative Spatiotemporal Modeling","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151509179","doi":"https://doi.org/10.1109/icmla66185.2025.00194"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052080007","display_name":"Sai Deepthi Yeddula","orcid":"https://orcid.org/0009-0008-3625-8069"},"institutions":[{"id":"https://openalex.org/I177721651","display_name":"Oakland University","ror":"https://ror.org/01ythxj32","country_code":"US","type":"education","lineage":["https://openalex.org/I177721651"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sai Deepthi Yeddula","raw_affiliation_strings":["Oakland University,Rochester,USA"],"affiliations":[{"raw_affiliation_string":"Oakland University,Rochester,USA","institution_ids":["https://openalex.org/I177721651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077803415","display_name":"Abhijeet Bhattacharya","orcid":null},"institutions":[{"id":"https://openalex.org/I177721651","display_name":"Oakland University","ror":"https://ror.org/01ythxj32","country_code":"US","type":"education","lineage":["https://openalex.org/I177721651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhijeet Bhattacharya","raw_affiliation_strings":["Oakland University,Rochester,USA"],"affiliations":[{"raw_affiliation_string":"Oakland University,Rochester,USA","institution_ids":["https://openalex.org/I177721651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133131754","display_name":"Chen Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Jiang","raw_affiliation_strings":["Auburn University,Auburn,USA"],"affiliations":[{"raw_affiliation_string":"Auburn University,Auburn,USA","institution_ids":["https://openalex.org/I82497590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052080007"],"corresponding_institution_ids":["https://openalex.org/I177721651"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74926167,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1273","last_page":"1278"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.15629999339580536,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.15629999339580536,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.12880000472068787,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12290","display_name":"Human Motion and Animation","score":0.05350000038743019,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.42250001430511475},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.40450000762939453},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.39340001344680786},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3571999967098236},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3073999881744385},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.2883000075817108}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.641700029373169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6168000102043152},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.39340001344680786},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31220000982284546},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2883000075817108},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320317124","display_name":"Oakland University","ror":"https://ror.org/01ythxj32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1978999584","https://openalex.org/W2029767187","https://openalex.org/W2111807801","https://openalex.org/W3081725187","https://openalex.org/W3133618741","https://openalex.org/W4377027995","https://openalex.org/W4390100375","https://openalex.org/W4414359302","https://openalex.org/W7133184152","https://openalex.org/W7140942257"],"related_works":[],"abstract_inverted_index":{"Irregularly":[0],"sampled":[1],"time":[2,99,119],"series":[3,100,120],"are":[4],"prevalent":[5],"in":[6,122],"domains":[7],"such":[8],"as":[9,111],"healthcare,":[10],"wearable":[11],"sensing,":[12],"and":[13,30,61,88,97],"geospatial":[14],"event":[15],"monitoring.":[16],"These":[17],"datasets":[18,75],"pose":[19],"significant":[20],"challenges":[21],"for":[22,116],"traditional":[23],"time-series":[24],"models,":[25],"which":[26],"assume":[27],"uniform":[28],"sampling":[29],"struggle":[31],"with":[32,126],"missing":[33],"or":[34],"delayed":[35],"observations.":[36],"In":[37],"this":[38],"work,":[39],"we":[40],"present":[41],"iDiffODE,":[42],"a":[43,112],"novel":[44],"continuous-time":[45],"generative":[46],"model":[47,90],"that":[48,86],"explicitly":[49],"constructs":[50],"smooth":[51],"latent":[52,95],"trajectories":[53],"from":[54],"irregular":[55,107,118],"observations,":[56],"enabling":[57],"both":[58],"accurate":[59],"reconstruction":[60],"realistic":[62],"simulation":[63],"of":[64,105],"multivariate":[65],"system":[66],"states.":[67],"We":[68],"evaluate":[69],"our":[70],"method":[71],"on":[72],"three":[73],"representative":[74],"demonstrating":[76],"consistent":[77],"improvements":[78],"over":[79],"the":[80,123],"existing":[81],"methods.":[82],"Our":[83],"results":[84],"demonstrate":[85],"iDiffODE":[87],"it\u2019s":[89],"variants":[91],"effectively":[92],"learn":[93],"continuous":[94],"dynamics":[96],"generate":[98],"data":[101,121],"even":[102],"under":[103],"conditions":[104],"severe":[106],"sampling,":[108],"positioning":[109],"it":[110],"highly":[113],"promising":[114],"solution":[115],"handling":[117],"real":[124],"world":[125],"enhanced":[127],"generalizability.":[128],"Github:":[129],"https://github.com/Abhijeet399/iDiffODE":[130]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}
