{"id":"https://openalex.org/W2946122132","doi":"https://doi.org/10.3390/e24010135","title":"An Information Theoretic Interpretation to Deep Neural Networks","display_name":"An Information Theoretic Interpretation to Deep Neural Networks","publication_year":2022,"publication_date":"2022-01-17","ids":{"openalex":"https://openalex.org/W2946122132","doi":"https://doi.org/10.3390/e24010135","mag":"2946122132","pmid":"https://pubmed.ncbi.nlm.nih.gov/35052161"},"language":"en","primary_location":{"id":"doi:10.3390/e24010135","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e24010135","pdf_url":"https://www.mdpi.com/1099-4300/24/1/135/pdf?version=1642505183","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/24/1/135/pdf?version=1642505183","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054043281","display_name":"Xiangxiang Xu","orcid":"https://orcid.org/0000-0002-4178-0934"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangxiang Xu","raw_affiliation_strings":["Data Science and Information Technology Research Center, Tsinghua\u2013Berkeley Shenzhen Institute, Shenzhen 518055, China"],"affiliations":[{"raw_affiliation_string":"Data Science and Information Technology Research Center, Tsinghua\u2013Berkeley Shenzhen Institute, Shenzhen 518055, China","institution_ids":["https://openalex.org/I4210114105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088293566","display_name":"Shao\u2010Lun Huang","orcid":"https://orcid.org/0000-0003-2827-4022"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shao-Lun Huang","raw_affiliation_strings":["Data Science and Information Technology Research Center, Tsinghua\u2013Berkeley Shenzhen Institute, Shenzhen 518055, China"],"affiliations":[{"raw_affiliation_string":"Data Science and Information Technology Research Center, Tsinghua\u2013Berkeley Shenzhen Institute, Shenzhen 518055, China","institution_ids":["https://openalex.org/I4210114105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112425882","display_name":"Lizhong Zheng","orcid":"https://orcid.org/0000-0002-6108-0222"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lizhong Zheng","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066172831","display_name":"Gregory W. Wornell","orcid":"https://orcid.org/0000-0001-9166-4758"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory W. Wornell","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5088293566"],"corresponding_institution_ids":["https://openalex.org/I4210114105"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":2.0699,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.88357916,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"24","issue":"1","first_page":"135","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9988999962806702,"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":0.9965999722480774,"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/computer-science","display_name":"Computer science","score":0.7497962117195129},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6891893744468689},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6154415607452393},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.5792260766029358},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5682143568992615},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5601396560668945},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4987185001373291},{"id":"https://openalex.org/keywords/information-theory","display_name":"Information theory","score":0.484843373298645},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.4400610327720642},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.42355048656463623},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3252534568309784},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1378178894519806},{"id":"https://openalex.org/keywords/cognitive-science","display_name":"Cognitive science","score":0.06371316313743591}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7497962117195129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6891893744468689},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6154415607452393},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.5792260766029358},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5682143568992615},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5601396560668945},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4987185001373291},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.484843373298645},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.4400610327720642},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.42355048656463623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3252534568309784},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1378178894519806},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.06371316313743591},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.3390/e24010135","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e24010135","pdf_url":"https://www.mdpi.com/1099-4300/24/1/135/pdf?version=1642505183","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:35052161","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35052161","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:dspace.mit.edu:1721.1/139647","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/139647","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/139647/1/entropy-24-00135-v2.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"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":"Multidisciplinary Digital Publishing Institute","raw_type":"http://purl.org/eprint/type/JournalArticle"},{"id":"pmh:oai:doaj.org/article:5fc6d37b5eca40c1b1caf4600ddd67f9","is_oa":true,"landing_page_url":"https://doaj.org/article/5fc6d37b5eca40c1b1caf4600ddd67f9","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 24, Iss 1, p 135 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/24/1/135/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e24010135","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy; Volume 24; Issue 1; Pages: 135","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8774347","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8774347","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e24010135","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e24010135","pdf_url":"https://www.mdpi.com/1099-4300/24/1/135/pdf?version=1642505183","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2382694725","display_name":"Collaborative Research: MLWiNS: Deep Neural Networks Meet Physical Layer Communications -- Learning with Knowledge of Structure","funder_award_id":"2002908","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2811237814","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G3405165046","display_name":null,"funder_award_id":"N00014-19-1-2621","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G6483256435","display_name":null,"funder_award_id":"61807021","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7012402599","display_name":null,"funder_award_id":"CNS-2002908","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2946122132.pdf","grobid_xml":"https://content.openalex.org/works/W2946122132.grobid-xml"},"referenced_works_count":66,"referenced_works":["https://openalex.org/W104184427","https://openalex.org/W1686810756","https://openalex.org/W1971947347","https://openalex.org/W1995228946","https://openalex.org/W2004026774","https://openalex.org/W2018582985","https://openalex.org/W2030748132","https://openalex.org/W2039103602","https://openalex.org/W2058815839","https://openalex.org/W2059120410","https://openalex.org/W2064675550","https://openalex.org/W2091891568","https://openalex.org/W2096516049","https://openalex.org/W2099111195","https://openalex.org/W2103496339","https://openalex.org/W2107327484","https://openalex.org/W2117539524","https://openalex.org/W2183341477","https://openalex.org/W2257979135","https://openalex.org/W2290247654","https://openalex.org/W2518143623","https://openalex.org/W2529194139","https://openalex.org/W2531409750","https://openalex.org/W2590082389","https://openalex.org/W2611269208","https://openalex.org/W2612445135","https://openalex.org/W2739748921","https://openalex.org/W2742980034","https://openalex.org/W2787894218","https://openalex.org/W2809090039","https://openalex.org/W2896457183","https://openalex.org/W2901004818","https://openalex.org/W2909671931","https://openalex.org/W2911867426","https://openalex.org/W2912222576","https://openalex.org/W2947000318","https://openalex.org/W2951583185","https://openalex.org/W2952502547","https://openalex.org/W2962772482","https://openalex.org/W2963000224","https://openalex.org/W2963095610","https://openalex.org/W2963423396","https://openalex.org/W2963446712","https://openalex.org/W2963715537","https://openalex.org/W2964184826","https://openalex.org/W2964350391","https://openalex.org/W2967916697","https://openalex.org/W2970656679","https://openalex.org/W2976504920","https://openalex.org/W2991489140","https://openalex.org/W2996320484","https://openalex.org/W3022414928","https://openalex.org/W3030163527","https://openalex.org/W3065974826","https://openalex.org/W3101552411","https://openalex.org/W3102159535","https://openalex.org/W3140968660","https://openalex.org/W4210869902","https://openalex.org/W4248624814","https://openalex.org/W4292779060","https://openalex.org/W6658043888","https://openalex.org/W6679436768","https://openalex.org/W6687483927","https://openalex.org/W6725543821","https://openalex.org/W6759437111","https://openalex.org/W6767728704"],"related_works":["https://openalex.org/W4361804730","https://openalex.org/W2142113611","https://openalex.org/W2334467465","https://openalex.org/W2087870008","https://openalex.org/W2162534555","https://openalex.org/W2752178021","https://openalex.org/W2107419853","https://openalex.org/W2143024819","https://openalex.org/W4247159817","https://openalex.org/W2964201926"],"abstract_inverted_index":{"With":[0],"the":[1,31,46,60,66,82,88,92,95,102,118],"unprecedented":[2],"performance":[3,78],"achieved":[4],"by":[5,111],"deep":[6,13],"learning,":[7],"it":[8],"is":[9],"commonly":[10],"believed":[11],"that":[12],"neural":[14],"networks":[15],"(DNNs)":[16],"attempt":[17],"to":[18,58,76],"extract":[19],"informative":[20],"features":[21],"for":[22,41,80],"learning":[23],"tasks.":[24],"To":[25],"formalize":[26],"this":[27],"intuition,":[28],"we":[29,53,106],"apply":[30],"local":[32],"information":[33],"geometric":[34],"analysis":[35,57],"and":[36,101,117],"establish":[37],"an":[38],"information-theoretic":[39,47,103],"framework":[40],"feature":[42,67],"selection,":[43],"which":[44,90],"demonstrates":[45],"optimality":[48],"of":[49,62,70,84,99],"DNN":[50],"features.":[51],"Moreover,":[52],"conduct":[54],"a":[55,77],"quantitative":[56],"characterize":[59],"impact":[61],"network":[63],"structure":[64],"on":[65,114],"extraction":[68],"process":[69,98],"DNNs.":[71],"Our":[72],"investigation":[73],"naturally":[74],"leads":[75],"metric":[79],"evaluating":[81],"effectiveness":[83],"extracted":[85],"features,":[86],"called":[87],"H-score,":[89],"illustrates":[91],"connection":[93],"between":[94],"practical":[96],"training":[97],"DNNs":[100],"framework.":[104],"Finally,":[105],"validate":[107],"our":[108],"theoretical":[109],"results":[110],"experimental":[112],"designs":[113],"synthesized":[115],"data":[116],"ImageNet":[119],"dataset.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
