{"id":"https://openalex.org/W7214762229","doi":"https://doi.org/10.48550/arxiv.2609.32517","title":"LocalProp: Neuro-Localized Memory-Efficient Backpropagation","display_name":"LocalProp: Neuro-Localized Memory-Efficient Backpropagation","publication_year":2026,"publication_date":"2026-09-26","ids":{"openalex":"https://openalex.org/W7214762229","doi":"https://doi.org/10.48550/arxiv.2609.32517"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.32517","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.32517","pdf_url":"https://arxiv.org/pdf/2609.32517","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,"listed_in":[],"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":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.32517","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017536017","display_name":"Diana-Nicoleta Grigore","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I141595442","display_name":"University of Bucharest","ror":"https://ror.org/02x2v6p15","country_code":"RO","type":"education","lineage":["https://openalex.org/I141595442"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Grigore, Diana-Nicoleta","raw_affiliation_strings":["Department of Computer Science University of Bucharest , Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Bucharest , Romania","institution_ids":["https://openalex.org/I141595442"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148051030","display_name":"Iuliana Georgescu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I141595442","display_name":"University of Bucharest","ror":"https://ror.org/02x2v6p15","country_code":"RO","type":"education","lineage":["https://openalex.org/I141595442"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Georgescu, Iuliana","raw_affiliation_strings":["Department of Computer Science University of Bucharest , Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Bucharest , Romania","institution_ids":["https://openalex.org/I141595442"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081017623","display_name":"Radu Tudor Ionescu","orcid":"https://orcid.org/0000-0002-9301-1950","observed_orcids":["https://orcid.org/0000-0002-9301-1950"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ionescu, Radu Tudor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5961999893188477,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5961999893188477,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.33640000224113464,"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":0.024700000882148743,"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/pre-training-and-fine-tuning","display_name":"pre-training and fine-tuning","score":0.781000018119812},{"id":"https://openalex.org/keywords/weight-pruning","display_name":"weight pruning","score":0.5230000019073486},{"id":"https://openalex.org/keywords/gpu-memory-reduction","display_name":"GPU memory reduction","score":0.7369999885559082},{"id":"https://openalex.org/keywords/biologically-plausible-learning","display_name":"biologically plausible learning","score":0.5809999704360962}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.7706999778747559},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7666000127792358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7289000153541565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6220999956130981},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41929998993873596},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.32517","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.32517","pdf_url":"https://arxiv.org/pdf/2609.32517","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,"listed_in":[],"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":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.32517","doi":"https://doi.org/10.48550/arxiv.2609.32517","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.32517","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.32517","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.32517","pdf_url":"https://arxiv.org/pdf/2609.32517","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,"listed_in":[],"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":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1008782263","display_name":null,"funder_award_id":"351416","funder_id":"https://openalex.org/F4320311068","funder_display_name":"Health Resources in Action"}],"funders":[{"id":"https://openalex.org/F4320311068","display_name":"Health Resources in Action","ror":"https://ror.org/01ctnwa22"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214762229.pdf","grobid_xml":"https://content.openalex.org/works/W7214762229.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W2338908902","https://openalex.org/W4313304716","https://openalex.org/W7160078252"],"related_works":["https://openalex.org/W7170932844","https://openalex.org/W4399554635","https://openalex.org/W4280557002","https://openalex.org/W3125127048","https://openalex.org/W4323022356","https://openalex.org/W2701971652","https://openalex.org/W4400023707","https://openalex.org/W4378576376","https://openalex.org/W7166797663","https://openalex.org/W3186222744"],"abstract_inverted_index":{"The":[0],"current":[1],"deep":[2],"learning":[3,32,93],"training":[4,12,44],"paradigm":[5],"employs":[6],"end-to-end":[7],"backpropagation,":[8],"regardless":[9],"of":[10,51,124,132],"the":[11,21,34,49,59,65,74,92,130,145],"stage,":[13],"i.e.":[14],"pre-training":[15,66],"or":[16],"fine-tuning.":[17],"However,":[18],"backpropagating":[19],"through":[20],"entire":[22],"model":[23],"is":[24,36,67,79],"neither":[25],"biologically":[26],"plausible":[27],"nor":[28],"memory":[29],"efficient,":[30],"since":[31],"inside":[33],"brain":[35],"highly":[37],"localized.":[38],"Therefore,":[39],"we":[40,136],"propose":[41],"LocalProp,":[42],"a":[43,52,76,83,122,141],"procedure":[45],"that":[46,116],"locally":[47,72],"updates":[48,57],"weights":[50],"model.":[53],"Our":[54],"neuro-localized":[55],"weight":[56],"follow":[58],"\"pre-training":[60],"then":[61],"fine-tuning\"":[62],"paradigm,":[63],"where":[64],"based":[68],"on":[69,104],"I-JEPA.":[70],"After":[71],"updating":[73],"weights,":[75],"pruning":[77],"operation":[78],"performed,":[80],"followed":[81],"by":[82,90],"short":[84],"final":[85],"fine-tuning":[86],"phase.":[87],"Pruning":[88],"helps":[89],"sending":[91],"signal":[94],"from":[95],"higher":[96],"blocks":[97],"to":[98],"lower":[99],"blocks.":[100],"We":[101],"perform":[102],"experiments":[103],"several":[105],"datasets,":[106],"including":[107],"large-scale":[108],"benchmarks":[109],"such":[110],"as":[111,140],"ImageNet,":[112],"and":[113],"empirically":[114],"show":[115],"LocalProp":[117],"reaches":[118],"good":[119],"performance":[120],"at":[121],"fraction":[123],"GPU":[125],"peak":[126],"memory.":[127],"By":[128],"varying":[129],"number":[131],"jointly":[133],"optimized":[134],"blocks,":[135],"identify":[137],"gradient-propagation":[138],"span":[139],"practical":[142],"control":[143],"over":[144],"accuracy-memory":[146],"trade-off.":[147]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-30T00:00:00"}