{"id":"https://openalex.org/W7220815542","doi":null,"title":"Deep models, lighter footprint compressing, explaining, and transferring Bayesian neural networks","display_name":"Deep models, lighter footprint compressing, explaining, and transferring Bayesian neural networks","publication_year":2025,"publication_date":"2025-05-01","ids":{"openalex":"https://openalex.org/W7220815542"},"language":"en","primary_location":{"id":"pmh:oai:www.ideals.illinois.edu:2142/129468","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2142/129468","pdf_url":"https://www.ideals.illinois.edu/items/136509/bitstreams/446148/data.pdf","source":{"id":"https://openalex.org/S4306401358","display_name":"Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801919071","host_organization_name":"University of Illinois System","host_organization_lineage":["https://openalex.org/I2801919071"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ideals.illinois.edu/items/136509/bitstreams/446148/data.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109407048","display_name":"Diptarka Saha","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Saha, Diptarka","raw_affiliation_strings":["College of the University of Illinois Urbana-Champaign , 2025 Urbana , Illinois"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of the University of Illinois Urbana-Champaign , 2025 Urbana , Illinois","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5109407048"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.6963000297546387,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.6963000297546387,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.133200004696846,"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":0.07280000299215317,"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/bayesian-neural-networks","display_name":"Bayesian neural networks","score":0.9990000128746033},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.7200000286102295},{"id":"https://openalex.org/keywords/spike-and-slab-prior","display_name":"spike-and-slab prior","score":0.9399999976158142},{"id":"https://openalex.org/keywords/variational-bayesian-inference","display_name":"variational inference","score":0.796999990940094},{"id":"https://openalex.org/keywords/structured-sparsity","display_name":"structured sparsity","score":0.6520000100135803},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6499999761581421},{"id":"https://openalex.org/keywords/sensitivity-analysis","display_name":"sensitivity analysis","score":0.7049999833106995},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.7149999737739563},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.7329999804496765},{"id":"https://openalex.org/keywords/power-prior","display_name":"power prior","score":0.8899999856948853},{"id":"https://openalex.org/keywords/pseudo-labeling","display_name":"pseudo-labeling","score":0.8579999804496765},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.8240000009536743}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5950000286102295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.58160001039505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5408999919891357},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.38580000400543213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32030001282691956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.319599986076355},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:www.ideals.illinois.edu:2142/129468","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2142/129468","pdf_url":"https://www.ideals.illinois.edu/items/136509/bitstreams/446148/data.pdf","source":{"id":"https://openalex.org/S4306401358","display_name":"Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801919071","host_organization_name":"University of Illinois System","host_organization_lineage":["https://openalex.org/I2801919071"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"pmh:oai:www.ideals.illinois.edu:2142/129468","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2142/129468","pdf_url":"https://www.ideals.illinois.edu/items/136509/bitstreams/446148/data.pdf","source":{"id":"https://openalex.org/S4306401358","display_name":"Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801919071","host_organization_name":"University of Illinois System","host_organization_lineage":["https://openalex.org/I2801919071"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7220815542.pdf","grobid_xml":"https://content.openalex.org/works/W7220815542.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W133229983","https://openalex.org/W1570197553","https://openalex.org/W1787224781","https://openalex.org/W1938500889","https://openalex.org/W1988115241","https://openalex.org/W2007069447","https://openalex.org/W2025713906","https://openalex.org/W2047229728","https://openalex.org/W2049228615","https://openalex.org/W2075281490","https://openalex.org/W2097533491","https://openalex.org/W2112796928","https://openalex.org/W2119144962","https://openalex.org/W2123045220","https://openalex.org/W2165698076","https://openalex.org/W2169393322","https://openalex.org/W2212676342","https://openalex.org/W2527824850","https://openalex.org/W2606744497","https://openalex.org/W2766678531","https://openalex.org/W2769421449","https://openalex.org/W2783538964","https://openalex.org/W2910655610","https://openalex.org/W2911535260","https://openalex.org/W2919115771","https://openalex.org/W2948509039","https://openalex.org/W3007769536","https://openalex.org/W3008186662","https://openalex.org/W3036525728","https://openalex.org/W3041133507","https://openalex.org/W3083624666","https://openalex.org/W3169336656","https://openalex.org/W4250518393","https://openalex.org/W4307135222","https://openalex.org/W4392251648","https://openalex.org/W4399785525","https://openalex.org/W4403936288","https://openalex.org/W4405853328","https://openalex.org/W4414298677","https://openalex.org/W6888914244","https://openalex.org/W6907697060","https://openalex.org/W6926495567","https://openalex.org/W6926739375","https://openalex.org/W6945218883","https://openalex.org/W6963913421","https://openalex.org/W6964095662"],"related_works":["https://openalex.org/W7113311542","https://openalex.org/W7111827409","https://openalex.org/W7163760905","https://openalex.org/W4404344813","https://openalex.org/W3215368795","https://openalex.org/W3148529081","https://openalex.org/W2781538374","https://openalex.org/W3213595901","https://openalex.org/W7133207734","https://openalex.org/W7099085854"],"abstract_inverted_index":{"Despite":[0],"their":[1,24,104],"widespread":[2],"adoption":[3],"and":[4,17,32,62,87,103,156,182,223,232,261],"impressive":[5],"empirical":[6],"performance,":[7],"modern":[8],"neural":[9,73,255],"networks":[10,74,256],"often":[11],"suffer":[12],"from":[13,178],"overparameterization,":[14],"limited":[15],"interpretability,":[16],"difficulty":[18],"in":[19,26,216,273],"knowledge":[20],"transfer.These":[21],"limitations":[22],"hinder":[23],"deployment":[25],"settings":[27],"where":[28,76],"computational":[29],"efficiency,":[30],"transparency,":[31],"adaptability":[33],"are":[34,258,264,271],"essential.This":[35],"thesis":[36],"addresses":[37],"these":[38,212],"challenges":[39],"through":[40,248],"a":[41,50,67,117,165,179,186,193,227],"unifying":[42],"principle:":[43],"building":[44],"Bayesian":[45,72,217,250],"deep":[46,239],"learning":[47,169,269],"models":[48,240],"with":[49,149,185,220],"lighter":[51],"footprint-models":[52],"that":[53,98,197,238,257,263],"retain":[54],"predictive":[55],"power":[56,172,302,313],"while":[57],"being":[58],"more":[59],"compact,":[60],"interpretable,":[61,260],"transferable.The":[63],"first":[64],"contribution":[65,115,163],"presents":[66],"structured":[68],"compression":[69],"framework":[70],"for":[71,153,281],"(BNNs),":[75],"model":[77,181,209],"sparsity":[78],"is":[79,96,214],"achieved":[80],"by":[81,130],"placing":[82],"spike-and-slab":[83],"priors":[84],"over":[85],"weights":[86,102],"inferring":[88],"posterior":[89,195],"inclusion":[90],"probabilities.A":[91],"scalable":[92],"variational":[93],"inference":[94],"algorithm":[95],"developed":[97],"simultaneously":[99],"learns":[100],"network":[101],"relevance,":[105],"enabling":[106],"principled":[107,194,249],"pruning":[108],"at":[109],"multiple":[110],"levels":[111],"of":[112,127,134,159,211,230,293],"granularity.The":[113],"second":[114],"proposes":[116],"sensitivity-based":[118],"feature":[119,145],"selection":[120,158],"method,":[121],"SENS,":[122],"which":[123],"quantifies":[124],"the":[125,132,138],"relevance":[126],"input":[128],"features":[129],"analyzing":[131],"distribution":[133],"local":[135],"sensitivities":[136],"under":[137],"BNN":[139],"posterior.This":[140],"method":[141],"provides":[142],"statistically":[143],"grounded":[144,215],"importance":[146],"scores":[147],"along":[148],"confidence":[150],"measures,":[151],"allowing":[152],"robust":[154],"interpretability":[155],"data-driven":[157],"relevant":[160],"inputs.The":[161],"third":[162],"introduces":[164],"semi-supervised,":[166],"source-free":[167],"transfer":[168,200],"approach":[170],"using":[171],"priors.By":[173],"generating":[174],"pseudo-labeled":[175],"auxiliary":[176,294],"data":[177,204,284],"black-box":[180],"combining":[183],"it":[184],"small":[187],"labeled":[188,282],"target":[189,283],"set,":[190],"we":[191,252],"derive":[192],"update":[196],"enables":[198],"adaptive":[199],"without":[201],"requiring":[202],"source":[203],"or":[205,246,276],"access":[206],"to":[207,288],"pretrained":[208],"internals.Each":[210],"contributions":[213],"methodology,":[218],"equipped":[219],"theoretical":[221],"guarantees,":[222],"empirically":[224],"validated":[225],"across":[226],"wide":[228],"range":[229],"synthetic":[231],"real-world":[233],"datasets.Taken":[234],"together,":[235],"they":[236],"demonstrate":[237],"need":[241],"not":[242],"be":[243],"large,":[244],"opaque,":[245],"rigid.Instead,":[247],"design,":[251],"can":[253],"construct":[254],"efficient,":[259],"resilient-qualities":[262],"increasingly":[265],"critical":[266],"as":[267],"machine":[268],"systems":[270],"deployed":[272],"high-stakes,":[274],"resource-constrained,":[275],"regulatory-sensitive":[277],"environments.":[278],"XDesign":[279],"matrix":[280,300],"y":[285],"Responses":[286],"corresponding":[287],"X":[289],"\u03a3":[290,296],"u":[291],"Variance":[292],"pseudo-labels":[295],"pos":[297,309],"Posterior":[298,310],"covariance":[299],"after":[301,312],"prior":[303,314],"adjustment":[304,315],"\u03b2":[305],"Regression":[306],"coefficients":[307],"\u00b5":[308],"mean":[311],"viii":[316]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-10-07T00:00:00"}