{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T19:10:04Z","timestamp":1746472204632,"version":"3.40.4"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research &#x0026; Develop Plan","award":["2023YFB4503600"],"award-info":[{"award-number":["2023YFB4503600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20299","U24B20144","62172424","62276270","62322214"],"award-info":[{"award-number":["U23A20299","U24B20144","62172424","62276270","62322214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/tkde.2025.3548298","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T18:57:06Z","timestamp":1741201026000},"page":"3499-3513","source":"Crossref","is-referenced-by-count":0,"title":["LIOF: Make the Learned Index Learn Faster With Higher Accuracy"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7671-5921","authenticated-orcid":false,"given":"Tao","family":"Ji","sequence":"first","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3608-5381","authenticated-orcid":false,"given":"Kai","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0538-2960","authenticated-orcid":false,"given":"Luming","family":"Sun","sequence":"additional","affiliation":[{"name":"Yunxi Technology Company Ltd., Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2620-7623","authenticated-orcid":false,"given":"Yiyan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0089-1045","authenticated-orcid":false,"given":"Cuiping","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8132-9382","authenticated-orcid":false,"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing, China"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1145\/356770.356776"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1145\/582318.582321"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1145\/3183713.3196909"},{"key":"ref4","first-page":"1992","article-title":"Learned index: A comprehensive experimental evaluation","volume-title":"Proc. VLDB Endowment","volume":"16","author":"Sun","year":"2023"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1145\/3318464.3389711"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1145\/3299869.3319860"},{"year":"2021","author":"Stoian","article-title":"PLEX: Towards practical learned indexing","key":"ref7"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.14778\/3389133.3389135"},{"key":"ref9","first-page":"308","article-title":"XIndex: A scalable learned index for multicore data storage","volume-title":"Proc. 25th ACM SIGPLAN Symp. Princ. Pract. Parallel Program.","author":"Tang"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.14778\/3457390.3457393"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.14778\/3407790.3407829"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1145\/3318464.3389703"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1109\/MDM.2019.00121"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1145\/3318464.3380579"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.14778\/3425879.3425880"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/ICDEW58674.2023.00015"},{"key":"ref17","first-page":"1572","article-title":"Efficiently learning spatial indices","volume-title":"Proc. IEEE 39th Int. Conf. Data Eng.","author":"Liu"},{"key":"ref18","first-page":"3123","article-title":"Why are learned indexes so effective","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Ferragina"},{"key":"ref19","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn"},{"year":"2019","author":"Rusu","article-title":"Meta-learning with latent embedding optimization","key":"ref20"},{"volume-title":"Proc. 2nd Int. Workshop Appl. AI Database Syst. Appl.","author":"Hadian","article-title":"MADEX: Learning-augmented algorithmic index structures","key":"ref21"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1145\/3401071.3401659","article-title":"RadixSpline: A single-pass learned index","volume-title":"Proc. 3rd Int. Workshop Exploiting Artif. Intell. Techn. Data Manage.","author":"Kipf"},{"key":"ref23","first-page":"1","article-title":"Updatable learned indexes meet disk-resident DBMS - From evaluations to design choices","volume-title":"Proc. ACM Manage. Data","volume":"1","author":"Lan","year":"2023"},{"key":"ref24","first-page":"407","article-title":"The ML-index: A multidimensional, learned index for point, range, and nearest-neighbor queries","volume-title":"Proc. Int. Conf. Extending Database Technol.","author":"Davitkova"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.14778\/3603581.3603598"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1145\/1071610.1071612"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1145\/3318464.3380579"},{"key":"ref28","first-page":"55","article-title":"COAX: Correlation-aware indexing","volume-title":"Proc. IEEE 39th Int. Conf. Data Eng. Workshops","author":"Hadian"},{"year":"2020","author":"Hadian","article-title":"Hands-off model integration in spatial index structures","key":"ref29"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1145\/3318464.3389770"},{"key":"ref31","first-page":"1","article-title":"The RLR-tree: A reinforcement learning based R-tree for spatial data","volume-title":"Proc. ACM Manage. Data","volume":"1","author":"Gu","year":"2023"},{"year":"2021","author":"Zhang","article-title":"Spatial interpolation-based learned index for range and KNN queries","key":"ref32"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.14778\/3598581.3598593"},{"key":"ref34","first-page":"2789","article-title":"CDFShop: Exploring and optimizing learned index structures","volume-title":"Proc. ACM SIGMOD Int. Conf. Manage. Data","author":"Marcus"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1109\/TKDE.2023.3342825"},{"volume-title":"Proc. Int. Workshop Appl. AI Database Syst. Appl.","author":"Kang","article-title":"The case for ML-enhanced high-dimensional indexes","key":"ref36"},{"year":"2023","author":"Froese","article-title":"Training neural networks is NP-hard in fixed dimension","key":"ref37"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"6696","DOI":"10.1109\/TSP.2020.3039360","article-title":"Approximation algorithms for training one-node ReLU neural networks","volume":"68","author":"Dey","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref39","first-page":"8871","article-title":"How fine-tuning allows for effective meta-learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chua"},{"year":"2023","author":"Kaur","article-title":"A comprehensive study on model initialization techniques ensuring efficient federated learning","key":"ref40"},{"key":"ref41","first-page":"28637","article-title":"How much does initialization affect generalization","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Ramasinghe"},{"key":"ref42","first-page":"1192","article-title":"Revisiting weight initialization of deep neural networks","volume-title":"Proc. 13th Asian Conf. Mach. Learn.","author":"Skorski"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","article-title":"A survey of deep meta-learning","volume":"54","author":"Huisman","year":"2020","journal-title":"Artif. Intell. Rev."},{"year":"2020","author":"Triantafillou","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","key":"ref44"},{"year":"2017","author":"Li","article-title":"Meta-SGD: Learning to learn quickly for few shot learning","key":"ref45"},{"key":"ref46","first-page":"7343","article-title":"Bayesian model-agnostic meta-learning","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Kim"},{"year":"2012","author":"Cortes","article-title":"L2 regularization for learning kernels","key":"ref47"},{"doi-asserted-by":"publisher","key":"ref48","DOI":"10.1109\/MPRV.2008.80"},{"year":"2019","author":"Kipf","article-title":"SOSD: A benchmark for learned indexes","key":"ref49"},{"year":"2021","article-title":"Line shapefiles","key":"ref50"},{"doi-asserted-by":"publisher","key":"ref51","DOI":"10.1145\/3187009.3177738"},{"key":"ref52","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification","volume-title":"Proc. IEEE Int. Conf. Comput. Vis.","author":"He"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/10981836\/10912756.pdf?arnumber=10912756","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T18:28:18Z","timestamp":1746469698000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10912756\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":52,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3548298","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"type":"print","value":"1041-4347"},{"type":"electronic","value":"1558-2191"},{"type":"electronic","value":"2326-3865"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}