{"id":"https://openalex.org/W7167811215","doi":"https://doi.org/10.48550/arxiv.2607.07156","title":"The Anatomy of Implicit Bias: Information Allocation in Neural Network Training","display_name":"The Anatomy of Implicit Bias: Information Allocation in Neural Network Training","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7167811215","doi":"https://doi.org/10.48550/arxiv.2607.07156"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.07156","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07156","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.07156","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140342572","display_name":"Zhang Gongyue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gongyue, Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5146901879","display_name":"Wang Zhiyong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiyong, Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140328897","display_name":"Liu Donghan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Donghan, Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140319251","display_name":"Ren Weihong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weihong, Ren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040373385","display_name":"\u738b\u667a\u52c7 Wang Zhiyong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yixuan, Sheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140377674","display_name":"Liu Honghai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Honghai, Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10320","display_name":"Neural Networks and Applications","score":0.4629000127315521,"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/T10320","display_name":"Neural Networks and Applications","score":0.4629000127315521,"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":0.17389999330043793,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.08030000329017639,"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/residual","display_name":"Residual","score":0.8574000000953674},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5673999786376953},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5572999715805054},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5382999777793884},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5249000191688538},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.39890000224113464},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.3276999890804291}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8574000000953674},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5673999786376953},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5572999715805054},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5382999777793884},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47049999237060547},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4390000104904175},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37619999051094055},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35899999737739563},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C2780388253","wikidata":"https://www.wikidata.org/wiki/Q5421508","display_name":"Exponent","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C2779832538","wikidata":"https://www.wikidata.org/wiki/Q2308809","display_name":"Coincidence","level":3,"score":0.2833999991416931},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2782999873161316},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.262800008058548},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.07156","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07156","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.07156","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07156","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Implicit":[0],"bias":[1,36,146,160],"is":[2,37,157,161,170],"usually":[3],"explained":[4],"as":[5],"the":[6,128,131,135,142,166,183],"preference":[7],"of":[8,33,73,134,144,214],"an":[9],"optimization":[10,52,225],"process":[11],"for":[12,57,223],"certain":[13],"final":[14,167],"solutions":[15],"and":[16,65,95,179,187,231],"their":[17],"geometry.":[18],"This":[19,41,68],"view":[20],"helps":[21],"explain":[22],"where":[23],"a":[24,44,54,71,109,203,211,221],"model":[25],"finally":[26],"stops.":[27],"It":[28,169,218],"gives":[29,210],"less":[30],"direct":[31],"explanation":[32,213],"how":[34],"this":[35,50,106,139,194,196],"formed":[38],"during":[39,190],"training.":[40,191],"paper":[42,69,107,140,197],"proposes":[43],"training-time":[45,151,215],"information":[46],"allocation":[47,75,123,133,233],"view.":[48],"Under":[49,112],"view,":[51,195],"forms":[53],"writing":[55],"pattern":[56],"error":[58,184],"signals":[59],"across":[60],"parameter":[61,175],"paths,":[62,176],"coordinate":[63,85,177],"channels,":[64,178],"sample":[66,180],"regions.":[67],"builds":[70],"set":[72],"observable":[74],"diagnostics.":[76],"These":[77],"diagnostics":[78],"include":[79],"gradient":[80],"demand,":[81],"actual":[82],"update":[83,93],"injection,":[84],"gain":[86],"induced":[87],"by":[88,165,173],"exponential":[89],"moving":[90],"averages,":[91],"channel-level":[92],"ratios,":[94],"sample-wise":[96],"loss":[97,118],"distributions.":[98],"To":[99],"separate":[100],"training":[101,114,136,200,229],"progress":[102,230],"from":[103,147],"internal":[104,122,132],"allocation,":[105],"introduces":[108],"collapse--persistence":[110],"analysis.":[111],"matched":[113],"loss,":[115],"if":[116],"external":[117],"statistics":[119],"collapse":[120],"but":[121],"ratios":[124],"remain":[125],"separated,":[126],"then":[127],"factor":[129],"changes":[130],"signal.":[137],"Overall,":[138],"extends":[141],"analysis":[143],"implicit":[145,159,216],"final-solution":[148],"geometry":[149],"to":[150],"signal":[152,185,232],"allocation.":[153],"The":[154,208],"main":[155],"claim":[156],"that":[158,227],"not":[162],"only":[163],"reflected":[164,172],"solution.":[168],"also":[171,219],"which":[174],"regions":[181],"receive":[182],"first":[186],"more":[188],"strongly":[189],"Based":[192],"on":[193],"places":[198],"different":[199],"factors":[201],"into":[202],"unified":[204],"information-allocation":[205],"diagnostic":[206],"framework.":[207],"framework":[209],"mechanism-level":[212],"bias.":[217],"provides":[220],"basis":[222],"future":[224],"methods":[226],"control":[228],"separately.":[234]},"counts_by_year":[],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2026-07-10T00:00:00"}
