{"id":"https://openalex.org/W7204146778","doi":"https://doi.org/10.1109/iccsc71566.2026.11650182","title":"HyperBench-7: A Multimodal Metadata for Hyperparameter Selection, Model Benchmarking, and AutoML Agent Evaluation","display_name":"HyperBench-7: A Multimodal Metadata for Hyperparameter Selection, Model Benchmarking, and AutoML Agent Evaluation","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7204146778","doi":"https://doi.org/10.1109/iccsc71566.2026.11650182"},"language":"en","primary_location":{"id":"doi:10.1109/iccsc71566.2026.11650182","doi":"https://doi.org/10.1109/iccsc71566.2026.11650182","is_oa":true,"landing_page_url":"https://doi.org/10.1109/iccsc71566.2026.11650182","pdf_url":null,"source":{"id":"https://openalex.org/S7407087460","display_name":"International Conference on Circuit, Systems and Communication (ICCSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Circuit, Systems and Communication (ICCSC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/iccsc71566.2026.11650182","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5147958480","display_name":"Nusrat Ahamed Kona","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405272712","display_name":"R. P. Shaha University","ror":"https://ror.org/04km2xa50","country_code":"BD","type":"education","lineage":["https://openalex.org/I4405272712"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Nusrat Ahamed Kona","raw_affiliation_strings":["R. P. Shaha University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R. P. Shaha University","institution_ids":["https://openalex.org/I4405272712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148040771","display_name":"Durjoy Saha Palok","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405272712","display_name":"R. P. Shaha University","ror":"https://ror.org/04km2xa50","country_code":"BD","type":"education","lineage":["https://openalex.org/I4405272712"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Durjoy Saha Palok","raw_affiliation_strings":["R. P. Shaha University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R. P. Shaha University","institution_ids":["https://openalex.org/I4405272712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093457058","display_name":"Hrithik Majumdar Shibu","orcid":"https://orcid.org/0009-0000-6818-4115","observed_orcids":["https://orcid.org/0009-0000-6818-4115"]},"institutions":[{"id":"https://openalex.org/I110002522","display_name":"University of Essex","ror":"https://ror.org/02nkf1q06","country_code":"GB","type":"education","lineage":["https://openalex.org/I110002522"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hrithik Majumdar Shibu","raw_affiliation_strings":["School of Computer Science and Electronic Engineering, University of Essex"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Electronic Engineering, University of Essex","institution_ids":["https://openalex.org/I110002522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111772142","display_name":"Shovan Kumar Paul","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405272712","display_name":"R. P. Shaha University","ror":"https://ror.org/04km2xa50","country_code":"BD","type":"education","lineage":["https://openalex.org/I4405272712"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Shovan Kumar Paul","raw_affiliation_strings":["R. P. Shaha University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R. P. Shaha University","institution_ids":["https://openalex.org/I4405272712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012884742","display_name":"Anupam Singha","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405272712","display_name":"R. P. Shaha University","ror":"https://ror.org/04km2xa50","country_code":"BD","type":"education","lineage":["https://openalex.org/I4405272712"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Anupam Singha","raw_affiliation_strings":["R. P. Shaha University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R. P. Shaha University","institution_ids":["https://openalex.org/I4405272712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030446078","display_name":"Kingkar Prosad Ghosh","orcid":"https://orcid.org/0009-0001-6170-2829","observed_orcids":["https://orcid.org/0009-0001-6170-2829"]},"institutions":[{"id":"https://openalex.org/I4405272712","display_name":"R. P. Shaha University","ror":"https://ror.org/04km2xa50","country_code":"BD","type":"education","lineage":["https://openalex.org/I4405272712"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Kingkar Prosad Ghosh","raw_affiliation_strings":["R. P. Shaha University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R. P. Shaha University","institution_ids":["https://openalex.org/I4405272712"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":0.55508752,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9994999766349792,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9994999766349792,"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/T11948","display_name":"Machine Learning in Materials Science","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":9.999999747378752e-05,"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/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.9169999957084656},{"id":"https://openalex.org/keywords/multimodal-dataset","display_name":"multimodal dataset","score":0.6769999861717224},{"id":"https://openalex.org/keywords/model-benchmarking","display_name":"model benchmarking","score":0.8510000109672546},{"id":"https://openalex.org/keywords/llm-based-agents","display_name":"LLM agents","score":0.7490000128746033},{"id":"https://openalex.org/keywords/meta-learning","display_name":"meta-learning","score":0.5609999895095825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6736999750137329},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.6198999881744385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5600000023841858},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39320001006126404},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3743000030517578},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3012000024318695},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C2780977526","wikidata":"https://www.wikidata.org/wiki/Q42417149","display_name":"Data exploration","level":3,"score":0.22360000014305115},{"id":"https://openalex.org/C62360110","wikidata":"https://www.wikidata.org/wiki/Q96777007","display_name":"Circumscription","level":2,"score":0.2190999984741211}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccsc71566.2026.11650182","doi":"https://doi.org/10.1109/iccsc71566.2026.11650182","is_oa":true,"landing_page_url":"https://doi.org/10.1109/iccsc71566.2026.11650182","pdf_url":null,"source":{"id":"https://openalex.org/S7407087460","display_name":"International Conference on Circuit, Systems and Communication (ICCSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Circuit, Systems and Communication (ICCSC)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/iccsc71566.2026.11650182","doi":"https://doi.org/10.1109/iccsc71566.2026.11650182","is_oa":true,"landing_page_url":"https://doi.org/10.1109/iccsc71566.2026.11650182","pdf_url":null,"source":{"id":"https://openalex.org/S7407087460","display_name":"International Conference on Circuit, Systems and Communication (ICCSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Circuit, Systems and Communication (ICCSC)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W4312116675","https://openalex.org/W4387455323","https://openalex.org/W4389994700","https://openalex.org/W4412396150"],"related_works":["https://openalex.org/W3109946716","https://openalex.org/W7131265388","https://openalex.org/W2509330591","https://openalex.org/W7133209880","https://openalex.org/W6968259196","https://openalex.org/W3037833767","https://openalex.org/W7162947352","https://openalex.org/W2897995638","https://openalex.org/W2911097174","https://openalex.org/W7125390637"],"abstract_inverted_index":{"Selecting":[0],"hyperparameters":[1,198],"in":[2],"machine":[3],"learning":[4],"presents":[5,22],"significant":[6],"challenges":[7],"due":[8],"to":[9,83,94],"the":[10,148,160],"requirement":[11],"of":[12,63,109,169],"numerous":[13],"trial-and-error":[14],"runs":[15],"or":[16],"expensive":[17],"optimizer-based":[18],"methods.":[19],"This":[20,192],"paper":[21],"HyperBench-7,":[23],"a":[24,106,120],"comprehensive":[25],"metadata":[26,52],"dataset":[27,54,76,117],"comprising":[28],"seven":[29],"modalities":[30],"(Image,":[31],"Text,":[32],"Tabular,":[33],"Graph,":[34],"Time-Series,":[35],"Audio,":[36],"and":[37,39,56,127,144,157,188],"Video)":[38],"their":[40],"optimal":[41],"hyperparameter":[42,80,130,186],"configurations.":[43],"The":[44,75,116],"collection":[45],"includes":[46],"2,000":[47],"configurations,":[48],"each":[49,73,84],"with":[50,101,175,184],"standardized":[51],"like":[53],"size":[55],"class":[57],"count,":[58],"enabling":[59],"detailed":[60],"quantitative":[61],"investigation":[62],"design":[64],"trends.":[65],"Exploratory":[66],"Data":[67],"Analysis":[68],"shows":[69],"unique":[70],"patterns":[71],"for":[72,89,124,139,152,204],"modality.":[74],"provides":[77,195],"\u201csafe":[78,196],"default\u201d":[79,197],"configurations":[81],"tailored":[82],"modality,":[85],"offering":[86],"reliable":[87],"baselines":[88],"new":[90,107,206],"datasets.":[91,115],"In":[92],"contrast":[93],"previous":[95],"benchmarks,":[96],"we":[97,134,179],"combine":[98],"multi-modal":[99,185],"data":[100],"specific":[102],"hyper-parameter":[103],"settings,":[104],"providing":[105],"level":[108],"model":[110],"performance":[111,131],"understanding":[112],"across":[113],"different":[114],"serves":[118],"as":[119,201],"valuable,":[121],"reusable":[122],"resource":[123],"meta-learning,":[125],"AutoML":[126,182],"research":[128,183],"into":[129],"dynamics.":[132],"Further,":[133],"evaluated":[135,151],"six":[136,149],"agent":[137,162,177],"types":[138],"predicting":[140,153],"model,":[141,154],"batch":[142,155],"size,":[143,156],"evaluation":[145,158],"metrics.":[146],"Among":[147],"agents":[150],"metrics,":[159],"LLM-based":[161],"performed":[163],"best,":[164],"achieving":[165],"an":[166],"overall":[167],"accuracy":[168],"66.7%":[170],"approximately.":[171],"By":[172],"combining":[173],"it":[174],"these":[176],"types,":[178],"can":[180],"advance":[181],"dynamics":[187],"well-founded":[189],"agentic":[190],"recommendations.":[191],"joint":[193],"approach":[194],"that":[199],"serve":[200],"good":[202],"benchmarks":[203],"any":[205],"dataset.":[207]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-08-25T00:00:00"}