{"id":"https://openalex.org/W4406459400","doi":"https://doi.org/10.1109/bigdata62323.2024.10825076","title":"Improving Neural Networks Dropout Using An Enhanced Weights Scaling","display_name":"Improving Neural Networks Dropout Using An Enhanced Weights Scaling","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406459400","doi":"https://doi.org/10.1109/bigdata62323.2024.10825076"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5022558724","display_name":"Aviv Yehezkel","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Aviv Yehezkel","raw_affiliation_strings":["Tel-Aviv,Israel"],"affiliations":[{"raw_affiliation_string":"Tel-Aviv,Israel","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5022558724"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26666002,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8868","last_page":"8870"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9983000159263611,"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":0.9980999827384949,"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/dropout","display_name":"Dropout (neural networks)","score":0.8942149877548218},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7067679166793823},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.6917375326156616},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6453233361244202},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5478838682174683},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4037739336490631},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12615063786506653}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.8942149877548218},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067679166793823},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.6917375326156616},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6453233361244202},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5478838682174683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4037739336490631},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12615063786506653},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1034159276","https://openalex.org/W1591801644","https://openalex.org/W1904365287","https://openalex.org/W2095705004","https://openalex.org/W2292443655","https://openalex.org/W2963654130","https://openalex.org/W3118608800","https://openalex.org/W4206569030","https://openalex.org/W4226428498","https://openalex.org/W4288364646","https://openalex.org/W4293714597","https://openalex.org/W4297797397","https://openalex.org/W6635446068","https://openalex.org/W6640036494","https://openalex.org/W6674330103","https://openalex.org/W6696879442","https://openalex.org/W6697814554","https://openalex.org/W6714142977","https://openalex.org/W6727785063","https://openalex.org/W6754484989","https://openalex.org/W6762269264","https://openalex.org/W6764043288","https://openalex.org/W6787972765","https://openalex.org/W6797110910"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Dropout":[0,29],"is":[1],"a":[2,11,35,65],"powerful":[3],"regularization":[4],"technique":[5],"that":[6],"successfully":[7],"mitigates":[8],"overfitting":[9],"across":[10],"wide":[12],"range":[13],"of":[14,31,38,52],"neural":[15],"network":[16],"architectures":[17],"and":[18,27,42,63],"applications,":[19],"from":[20],"computer":[21],"vision":[22],"to":[23,48,72],"natural":[24],"language":[25],"processing":[26],"more.":[28],"consists":[30],"dropping":[32],"(i.e.,":[33],"zeroing)":[34],"random":[36],"fraction":[37],"the":[39,45,49,53,78],"layer\u2019s":[40],"nodes":[41],"then":[43],"scaling":[44,62],"weights":[46],"according":[47],"complement":[50],"probability":[51],"dropout":[54],"rate.":[55],"This":[56],"paper":[57],"proposes":[58],"an":[59],"enhanced":[60],"weight":[61],"uses":[64],"simulation":[66],"study":[67],"over":[68,77],"two":[69],"benchmark":[70],"datasets":[71],"demonstrate":[73],"its":[74],"improved":[75],"accuracy":[76],"standard":[79],"approach.":[80]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}