{"id":"https://openalex.org/W7137940517","doi":"https://doi.org/10.1609/aaai.v40i28.39527","title":"Flow-Induced Diagonal Gaussian Processes","display_name":"Flow-Induced Diagonal Gaussian Processes","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137940517","doi":"https://doi.org/10.1609/aaai.v40i28.39527"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i28.39527","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i28.39527","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i28.39527","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031820538","display_name":"Moule Lin","orcid":"https://orcid.org/0000-0001-6227-2392"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Moule Lin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003864062","display_name":"Andrea Patan\u00e8","orcid":"https://orcid.org/0000-0003-0492-4860"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrea Patane","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121976514","display_name":"Weipeng Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weipeng Jing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102684501","display_name":"Shuhao Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuhao Guan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125727120","display_name":"Goetz Botterweck","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goetz Botterweck","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031820538"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15195292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"28","first_page":"23550","last_page":"23558"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.7738999724388123,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.7738999724388123,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.09640000015497208,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.02290000021457672,"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/residual","display_name":"Residual","score":0.5408999919891357},{"id":"https://openalex.org/keywords/diagonal","display_name":"Diagonal","score":0.5397999882698059},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.534500002861023},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5343999862670898},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5253999829292297},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.508400022983551},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.45239999890327454},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4440999925136566},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4302999973297119},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.414900004863739}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6273000240325928},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5703999996185303},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.5397999882698059},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.534500002861023},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5253999829292297},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.508400022983551},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.45239999890327454},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4302999973297119},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3743000030517578},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.35850000381469727},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3228999972343445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3181999921798706},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C65557600","wikidata":"https://www.wikidata.org/wiki/Q7249451","display_name":"Projection method","level":3,"score":0.2863999903202057},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.25459998846054077},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i28.39527","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i28.39527","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i28.39527","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i28.39527","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"Flow-Induced":[2],"Diagonal":[3],"Gaussian":[4],"Processes":[5],"(FiD-GP),":[6],"a":[7,12,19,25,52,71,130],"compression":[8],"framework":[9,64],"that":[10,82],"incorporates":[11],"compact":[13],"inducing":[14,46,125],"weight":[15,22],"matrix":[16],"to":[17,39,69],"project":[18],"neural":[20,109],"network\u2019s":[21,110],"uncertainty":[23,85,166],"into":[24],"lower-dimensional":[26],"subspace.":[27],"Critically,":[28],"FiD-GP":[29,66,83],"relies":[30],"on":[31,88,121],"normalising":[32],"flow":[33],"variational":[34],"posterior":[35],"and":[36,43,105,140,161,165],"spectral":[37,97],"regularisations":[38],"augment":[40],"its":[41],"expressiveness":[42],"align":[44],"the":[45,62,108,114,122],"subspace":[47],"with":[48,92,100],"feature-gradient":[49],"geometry":[50],"through":[51],"numerically":[53],"stable":[54],"projection":[55,74],"mechanism":[56],"objective.":[57],"Furthermore,":[58],"we":[59],"demonstrate":[60],"how":[61],"prediction":[63],"in":[65,129],"can":[67],"help":[68],"design":[70],"single":[72],"pass":[73],"for":[75],"Out-of-Distribution":[76,141],"(OoD)":[77],"detection.":[78],"Our":[79],"analysis":[80],"shows":[81],"improves":[84],"estimation":[86],"ability":[87],"various":[89],"tasks":[90],"compared":[91],"SVGP-based":[93],"baselines,":[94],"satisfies":[95],"tight":[96],"residual":[98],"bounds":[99],"theoretically":[101],"guaranteed":[102],"OoD":[103],"detection,":[104],"significantly":[106,145],"compresses":[107,150],"storage":[111],"requirements":[112],"at":[113],"cost":[115],"of":[116,124],"increased":[117],"inference":[118],"computation":[119],"dependent":[120],"number":[123],"weights":[126],"employed.":[127],"Specifically,":[128],"comprehensive":[131],"empirical":[132],"study":[133],"spanning":[134],"regression,":[135],"image":[136],"classification,":[137],"semantic":[138],"segmentation,":[139],"detection":[142],"benchmarks,":[143],"it":[144],"cuts":[146],"Bayesian":[147],"training":[148],"cost,":[149],"parameters":[151],"by":[152,158],"roughly":[153],"51%,":[154],"reduces":[155],"model":[156],"size":[157],"about":[159],"75%,":[160],"matches":[162],"state-of-the-art":[163],"accuracy":[164],"estimation.":[167]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}
