{"id":"https://openalex.org/W4285805306","doi":"https://doi.org/10.1145/3520304.3528813","title":"Synaptic pruning with MAP-elites","display_name":"Synaptic pruning with MAP-elites","publication_year":2022,"publication_date":"2022-07-09","ids":{"openalex":"https://openalex.org/W4285805306","doi":"https://doi.org/10.1145/3520304.3528813"},"language":"en","primary_location":{"id":"doi:10.1145/3520304.3528813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3520304.3528813","pdf_url":null,"source":{"id":"https://openalex.org/S4363608771","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073046118","display_name":"Federico Da Rold","orcid":"https://orcid.org/0009-0007-7336-5202"},"institutions":[{"id":"https://openalex.org/I26120043","display_name":"Ochanomizu University","ror":"https://ror.org/03599d813","country_code":"JP","type":"education","lineage":["https://openalex.org/I26120043"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Federico Da Rold","raw_affiliation_strings":["Ochanomizu University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ochanomizu University, Tokyo, Japan","institution_ids":["https://openalex.org/I26120043"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083965796","display_name":"Olaf Witkovski","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Olaf Witkovski","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002767684","display_name":"Nathana\u00ebl Aubert-Kato","orcid":"https://orcid.org/0000-0002-9100-1855"},"institutions":[{"id":"https://openalex.org/I26120043","display_name":"Ochanomizu University","ror":"https://ror.org/03599d813","country_code":"JP","type":"education","lineage":["https://openalex.org/I26120043"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nathanael Aubert-Kato","raw_affiliation_strings":["Ochanomizu University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ochanomizu University, Tokyo, Japan","institution_ids":["https://openalex.org/I26120043"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06652956,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"639","last_page":"642"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9993000030517578,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8360418677330017},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7471005916595459},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7176169753074646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7116653919219971},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6855912208557129},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6310950517654419},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5981962084770203},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4994680881500244},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.4972379505634308},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4967718720436096},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4864963889122009},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.461595743894577},{"id":"https://openalex.org/keywords/uncorrelated","display_name":"Uncorrelated","score":0.4547317922115326},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4391351640224457},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09774991869926453},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06394854187965393}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8360418677330017},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7471005916595459},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7176169753074646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7116653919219971},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6855912208557129},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6310950517654419},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5981962084770203},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4994680881500244},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.4972379505634308},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4967718720436096},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4864963889122009},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.461595743894577},{"id":"https://openalex.org/C169345407","wikidata":"https://www.wikidata.org/wiki/Q8216221","display_name":"Uncorrelated","level":2,"score":0.4547317922115326},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4391351640224457},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09774991869926453},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06394854187965393},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3520304.3528813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3520304.3528813","pdf_url":null,"source":{"id":"https://openalex.org/S4363608771","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1738827650","https://openalex.org/W2097351860","https://openalex.org/W2145339207","https://openalex.org/W2559655401","https://openalex.org/W2906697496","https://openalex.org/W3021119228","https://openalex.org/W3046262286","https://openalex.org/W3159274191","https://openalex.org/W4212774754"],"related_works":["https://openalex.org/W4296474751","https://openalex.org/W3210045201","https://openalex.org/W3190203064","https://openalex.org/W4287025741","https://openalex.org/W3147214434","https://openalex.org/W3034946435","https://openalex.org/W4312200629","https://openalex.org/W4280592718","https://openalex.org/W3186919929","https://openalex.org/W4312863455"],"abstract_inverted_index":{"Reducing":[0],"the":[1,18,24,48,77,85,103,123],"number":[2,38],"of":[3,20,22,39,76,80,106,117],"parameters":[4],"in":[5,13,27,91,125],"deep":[6,62],"learning":[7,30,57,63],"models":[8],"is":[9],"a":[10,28,36,53,61,74],"current":[11],"challenge":[12],"machine":[14],"learning.":[15,95],"We":[16],"exploit":[17],"capability":[19],"MAP-Elites":[21],"illuminating":[23],"search":[25],"space":[26],"reinforcement":[29,56],"problem,":[31],"confronting":[32],"neural":[33],"networks":[34],"with":[35],"different":[37],"connections.":[40],"In":[41],"this":[42],"work,":[43],"we":[44],"focus":[45],"specifically":[46],"on":[47,60],"Open":[49],"AI":[50],"BipedalWalker":[51],"[2],":[52],"widely":[54],"employed":[55,90],"benchmark,":[58],"and":[59,108,115],"model":[64],"that":[65,68,112],"successfully":[66],"solved":[67],"task.":[69],"The":[70],"resulting":[71],"architectures":[72],"show":[73,111],"reduction":[75],"synaptic":[78],"connectivity":[79],"approximately":[81],"90%,":[82],"equivalent":[83],"to":[84,101,122],"state-of-the-art":[86],"pruning":[87],"techniques":[88],"usually":[89],"supervised":[92],"or":[93],"generative":[94],"Specifically,":[96],"our":[97,126],"approach":[98],"allows":[99],"us":[100],"evaluate":[102],"respective":[104],"impacts":[105],"sparsity":[107,114],"dropout.":[109],"Results":[110],"both":[113],"dropouts":[116],"hidden":[118],"units":[119],"are":[120],"uncorrelated":[121],"performance":[124],"generalization":[127],"test.":[128]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}