{"id":"https://openalex.org/W4415949654","doi":"https://doi.org/10.48550/arxiv.2510.16075","title":"Optimization of the quantization of dense neural networks from an exact QUBO formulation","display_name":"Optimization of the quantization of dense neural networks from an exact QUBO formulation","publication_year":2025,"publication_date":"2025-10-17","ids":{"openalex":"https://openalex.org/W4415949654","doi":"https://doi.org/10.48550/arxiv.2510.16075"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2510.16075","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.16075","pdf_url":"https://arxiv.org/pdf/2510.16075","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.16075","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118616735","display_name":"Sergio Mu\u00f1iz Subi\u00f1as","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Subi\u00f1as, Sergio Mu\u00f1iz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013280218","display_name":"Manuel Gonz\u00e1lez","orcid":"https://orcid.org/0000-0003-4336-6224"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gonz\u00e1lez, Manuel L.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"G\u00f3mez, Jorge Ruiz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00f3mez, Jorge Ruiz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102766432","display_name":"Alejandro Mata Ali","orcid":"https://orcid.org/0009-0006-7289-8827"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ali, Alejandro Mata","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105886122","display_name":"Jorge M. Martin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mart\u00edn, Jorge Mart\u00ednez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051151030","display_name":"Miguel Franco Hernando","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hernando, Miguel Franco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058593839","display_name":"\u00c1ngel Miguel Garc\u00eda-Vico","orcid":"https://orcid.org/0000-0003-1583-2128"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garc\u00eda-Vico, \u00c1ngel Miguel","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":0.5056999921798706,"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":0.5056999921798706,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.20260000228881836,"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/T10057","display_name":"Face and Expression Recognition","score":0.04320000112056732,"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/quantization","display_name":"Quantization (signal processing)","score":0.6696000099182129},{"id":"https://openalex.org/keywords/rounding","display_name":"Rounding","score":0.626800000667572},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.569100022315979},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5202999711036682},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.4674000144004822},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.37940001487731934},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.37470000982284546}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6696000099182129},{"id":"https://openalex.org/C136625980","wikidata":"https://www.wikidata.org/wiki/Q663208","display_name":"Rounding","level":2,"score":0.626800000667572},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.569100022315979},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4982999861240387},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.4674000144004822},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4348999857902527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42410001158714294},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3709999918937683},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.326200008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C97137487","wikidata":"https://www.wikidata.org/wiki/Q729138","display_name":"Integer (computer science)","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C177179195","wikidata":"https://www.wikidata.org/wiki/Q7268372","display_name":"Quadratic unconstrained binary optimization","level":4,"score":0.2840000092983246},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C52692508","wikidata":"https://www.wikidata.org/wiki/Q1333872","display_name":"Combinatorial optimization","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.16075","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.16075","pdf_url":"https://arxiv.org/pdf/2510.16075","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.16075","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.16075","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.16075","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.16075","pdf_url":"https://arxiv.org/pdf/2510.16075","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"This":[0],"work":[1],"introduces":[2],"a":[3,13,110],"post-training":[4],"quantization":[5,113],"(PTQ)":[6],"method":[7],"for":[8,47],"dense":[9],"neural":[10],"networks":[11],"via":[12],"novel":[14],"ADAROUND-based":[15],"QUBO":[16,39,62],"formulation.":[17],"Using":[18],"the":[19,23,27,31,35,44,57,60,64],"Frobenius":[20],"distance":[21],"between":[22],"theoretical":[24],"output":[25,29],"and":[26,50,98,107],"dequantized":[28],"(before":[30],"activation":[32],"function)":[33],"as":[34,87],"objective,":[36],"an":[37],"explicit":[38],"whose":[40],"binary":[41],"variables":[42],"represent":[43],"rounding":[45],"choice":[46],"each":[48],"weight":[49],"bias":[51],"is":[52,92],"obtained.":[53],"Additionally,":[54],"by":[55],"exploiting":[56],"structure":[58],"of":[59,75],"coefficient":[61],"matrix,":[63],"global":[65],"problem":[66],"can":[67,79],"be":[68,80],"exactly":[69],"decomposed":[70],"into":[71],"$n$":[72],"independent":[73],"subproblems":[74],"size":[76],"$f+1$,":[77],"which":[78],"efficiently":[81],"solved":[82],"using":[83],"some":[84],"heuristics":[85],"such":[86],"simulated":[88],"annealing.":[89],"The":[90],"approach":[91],"evaluated":[93],"on":[94],"MNIST,":[95],"Fashion-MNIST,":[96],"EMNIST,":[97],"CIFAR-10":[99],"across":[100],"integer":[101],"precisions":[102],"from":[103],"int8":[104],"to":[105],"int1":[106],"compared":[108],"with":[109],"round-to-nearest":[111],"traditional":[112],"methodology.":[114]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-22T00:00:00"}