{"id":"https://openalex.org/W7053115686","doi":null,"title":"Training Neural Networks with Discrete Optimization Solvers","display_name":"Training Neural Networks with Discrete Optimization Solvers","publication_year":2020,"publication_date":"2020-07-13","ids":{"openalex":"https://openalex.org/W7053115686"},"language":"en","primary_location":{"id":"pmh:oai:tudelft.nl:uuid:27875d51-672a-4a78-9aad-c44236992518","doi":null,"is_oa":true,"landing_page_url":"http://resolver.tudelft.nl/uuid:27875d51-672a-4a78-9aad-c44236992518","pdf_url":"https://repository.tudelft.nl/file/File_d1c9b727-5e36-4b00-910d-dcb2d57680bd","source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"master thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://repository.tudelft.nl/file/File_d1c9b727-5e36-4b00-910d-dcb2d57680bd","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053461267","display_name":"T\u00f3mas Thorbjarnarson","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"\u00deorbjarnarson, T. (author)","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5053461267"],"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":true,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.44679999351501465,"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":0.44679999351501465,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.28299999237060547,"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/T10320","display_name":"Neural Networks and Applications","score":0.14900000393390656,"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/neural-network-training","display_name":"neural network training","score":0.9589999914169312},{"id":"https://openalex.org/keywords/mixed-integer-programming","display_name":"mixed integer programming","score":0.9010000228881836},{"id":"https://openalex.org/keywords/mip-solvers","display_name":"MIP solvers","score":0.8180000185966492},{"id":"https://openalex.org/keywords/binary-neural-networks","display_name":"binary neural networks","score":0.9210000038146973},{"id":"https://openalex.org/keywords/fairness","display_name":"fairness","score":0.38100001215934753},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.9959999918937683},{"id":"https://openalex.org/keywords/batch-training","display_name":"batch training","score":0.9900000095367432},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.5329999923706055},{"id":"https://openalex.org/keywords/generalization","display_name":"generalization","score":0.6779999732971191}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7922000288963318},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6909000277519226},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6172000169754028},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5810999870300293},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5267999768257141},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5213000178337097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5113000273704529},{"id":"https://openalex.org/C56086750","wikidata":"https://www.wikidata.org/wiki/Q6042592","display_name":"Integer programming","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47110000252723694},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.46650001406669617},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.44850000739097595},{"id":"https://openalex.org/C97137487","wikidata":"https://www.wikidata.org/wiki/Q729138","display_name":"Integer (computer science)","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3160000145435333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29760000109672546},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:tudelft.nl:uuid:27875d51-672a-4a78-9aad-c44236992518","doi":null,"is_oa":true,"landing_page_url":"http://resolver.tudelft.nl/uuid:27875d51-672a-4a78-9aad-c44236992518","pdf_url":"https://repository.tudelft.nl/file/File_d1c9b727-5e36-4b00-910d-dcb2d57680bd","source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"master thesis"}],"best_oa_location":{"id":"pmh:oai:tudelft.nl:uuid:27875d51-672a-4a78-9aad-c44236992518","doi":null,"is_oa":true,"landing_page_url":"http://resolver.tudelft.nl/uuid:27875d51-672a-4a78-9aad-c44236992518","pdf_url":"https://repository.tudelft.nl/file/File_d1c9b727-5e36-4b00-910d-dcb2d57680bd","source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"master thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7053115686.pdf","grobid_xml":"https://content.openalex.org/works/W7053115686.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W2100805904","https://openalex.org/W2112796928","https://openalex.org/W2346438296","https://openalex.org/W2524428287","https://openalex.org/W2592929672","https://openalex.org/W2594265094","https://openalex.org/W2623127191","https://openalex.org/W2721006554","https://openalex.org/W2765185632","https://openalex.org/W2787513570","https://openalex.org/W2802022891","https://openalex.org/W2803113791","https://openalex.org/W2807040120","https://openalex.org/W2886014761","https://openalex.org/W2895089194","https://openalex.org/W2946948417","https://openalex.org/W2963600714","https://openalex.org/W2964330541","https://openalex.org/W3035026192","https://openalex.org/W3035160371","https://openalex.org/W4255949318"],"related_works":["https://openalex.org/W3083971896","https://openalex.org/W4318754352","https://openalex.org/W2973514284","https://openalex.org/W3210540428","https://openalex.org/W4221139667","https://openalex.org/W4407271028","https://openalex.org/W7137920976","https://openalex.org/W4289283459","https://openalex.org/W4402886352","https://openalex.org/W4387933920"],"abstract_inverted_index":{"Recent":[0],"work":[1,78,94,115,178],"has":[2,23],"shown":[3],"potential":[4,127,198],"in":[5,199],"using":[6,87],"Mixed":[7],"Integer":[8],"Programming":[9],"(MIP)":[10],"solvers":[11,60],"to":[12,36,100,116,119,134,147,162,183,203,218,228],"optimize":[13],"certain":[14],"aspects":[15],"of":[16,32,46,75,104,129,152],"neural":[17],"networks":[18],"(NN).":[19],"However":[20],"little":[21],"research":[22],"gone":[24],"into":[25],"training":[26,57,145,153,214,230],"NNs":[27,38,86,131],"with":[28,58,132,189,201],"MIP":[29,59,88,98],"solvers.":[30,89],"State":[31],"the":[33,102,126,150],"art":[34],"methods":[35,185],"train":[37,84,120],"are":[39],"typically":[40],"gradient-based":[41,184,229],"and":[42,51,136,179,205,224],"require":[43,63],"significant":[44],"amounts":[45,74],"data,":[47,221],"computation":[48],"on":[49,80],"GPUs":[50,64],"extensive":[52],"hyper-parameter":[53,66],"tuning.":[54],"In":[55],"contrast,":[56],"should":[61],"not":[62,71],"or":[65],"tuning":[67],"but":[68],"can":[69,107,156,186,215],"likely":[70],"handle":[72],"large":[73],"data.":[76,191],"This":[77],"builds":[79],"recent":[81,177],"advances":[82],"that":[83,106,155,172,180,195,212],"binarized":[85],"We":[90,111,124,139,159],"go":[91],"beyond":[92],"current":[93,114],"by":[95],"formulating":[96],"new":[97],"models":[99],"increase":[101,149,222],"amount":[103,151],"data":[105,154],"be":[108,117,157,187,216],"feasibly":[109],"used.":[110,158],"also":[112,140],"extend":[113],"able":[118],"non-binary":[121],"integer-valued":[122,232],"networks.":[123,233],"show":[125,171,211],"benefits":[128],"optimizing":[130,200],"regards":[133,202],"fairness":[135,204],"model":[137,206],"compression.":[138,207],"propose":[141],"a":[142],"novel":[143],"batch":[144,213],"method":[146],"considerably":[148],"conduct":[160],"experiments":[161],"test":[163],"our":[164,209],"proposed":[165],"methodology.":[166],"The":[167],"experimental":[168],"results":[169,182,210,227],"firstly":[170],"we":[173,193],"have":[174],"improved":[175],"upon":[176],"comparable":[181,226],"achieved":[188],"minimal":[190],"Secondly,":[192],"find":[194],"there":[196],"is":[197],"Finally,":[208],"used":[217],"utilize":[219],"more":[220],"generalization":[223],"reach":[225],"for":[231]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}