{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T22:02:15Z","timestamp":1770847335467,"version":"3.50.1"},"reference-count":27,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100009027","name":"Japan Science and Technology Agency\u2013Advanced Low Carbon Technology Research and Development Program(JST-ALCA-Next), Japan","doi-asserted-by":"publisher","award":["JPMJAN24F3"],"award-info":[{"award-number":["JPMJAN24F3"]}],"id":[{"id":"10.13039\/501100009027","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Japan Science and Technology Agency\u2013Broadening Opportunities for Outstanding Young Researchers and Doctoral Students in Strategic Areas, Japan,","award":["JPMJBS2430"],"award-info":[{"award-number":["JPMJBS2430"]}]},{"name":"Japan Science and Technology Agency\u2013Broadening Opportunities for Outstanding Young Researchers and Doctoral Students in Strategic Areas, Japan,","award":["JPMJBY24G7"],"award-info":[{"award-number":["JPMJBY24G7"]}]},{"DOI":"10.13039\/501100000266","name":"U.K. Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","award":["EP\/W032635\/1"],"award-info":[{"award-number":["EP\/W032635\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000266","name":"U.K. Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","award":["EP\/V028251\/1"],"award-info":[{"award-number":["EP\/V028251\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000266","name":"U.K. Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","award":["EP\/S030069\/1"],"award-info":[{"award-number":["EP\/S030069\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000266","name":"U.K. Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","award":["EP\/X036006\/1"],"award-info":[{"award-number":["EP\/X036006\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000266","name":"U.K. Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","award":["EP\/W03221X\/1"],"award-info":[{"award-number":["EP\/W03221X\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["256;"],"award-info":[{"award-number":["256;"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Altera;"},{"name":"Intel;"},{"name":"AMD."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3659474","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T21:04:29Z","timestamp":1769807069000},"page":"19424-19439","source":"Crossref","is-referenced-by-count":0,"title":["Memory-Efficient and Trustworthy Neural Networks via Random Seed-Based Design"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6907-1524","authenticated-orcid":false,"given":"Hiroaki","family":"Ito","sequence":"first","affiliation":[{"name":"1Institute of Science Tokyo, Yokohama, Kanagawa, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8418-4691","authenticated-orcid":false,"given":"Hikari","family":"Otsuka","sequence":"additional","affiliation":[{"name":"1Institute of Science Tokyo, Yokohama, Kanagawa, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2009-7105","authenticated-orcid":false,"given":"Ryota","family":"Yasudo","sequence":"additional","affiliation":[{"name":"2Kyoto University, Kyoto, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9263-6529","authenticated-orcid":false,"given":"Zhiqiang","family":"Que","sequence":"additional","affiliation":[{"name":"3Imperial College London, London, U.K."}]},{"given":"Jose G. F.","family":"Coutinho","sequence":"additional","affiliation":[{"name":"3Imperial College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7949-0417","authenticated-orcid":false,"given":"Daichi","family":"Fujiki","sequence":"additional","affiliation":[{"name":"1Institute of Science Tokyo, Yokohama, Kanagawa, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1543-1252","authenticated-orcid":false,"given":"Masato","family":"Motomura","sequence":"additional","affiliation":[{"name":"1Institute of Science Tokyo, Yokohama, Kanagawa, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0272-9175","authenticated-orcid":false,"given":"Ce","family":"Guo","sequence":"additional","affiliation":[{"name":"3Imperial College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6750-927X","authenticated-orcid":false,"given":"Wayne","family":"Luk","sequence":"additional","affiliation":[{"name":"3Imperial College London, London, U.K."}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01191"},{"key":"ref2","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Gal"},{"key":"ref3","first-page":"3597","article-title":"Deconstructing lottery tickets: Zeros, signs, and the supermask","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Zhou"},{"key":"ref4","first-page":"1","article-title":"Partially frozen random networks contain compact strong lottery tickets","author":"Otsuka","year":"2025","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3160948"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3728179.3728198"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref8","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kipf"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3153682"},{"key":"ref10","first-page":"6682","article-title":"Proving the lottery ticket hypothesis: Pruning is all you need","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Malach"},{"key":"ref11","first-page":"2599","article-title":"Optimal lottery tickets via subset sum: Logarithmic over-parameterization is sufficient","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Pensia"},{"key":"ref12","first-page":"1","article-title":"Uncovering strong lottery tickets in graph transformers: A path to memory efficient and robust graph learning","author":"Ito","year":"2025","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref13","first-page":"30277","article-title":"GraphDE: A generative framework for debiased learning and out-of-distribution detection on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Li"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICFPT52863.2021.9609847"},{"key":"ref15","article-title":"Uncertainty in graph neural networks: A survey","author":"Wang","year":"2024","journal-title":"arXiv:2403.07185"},{"key":"ref16","first-page":"325","article-title":"Enabling fast uncertainty estimation: Accelerating Bayesian transformers via algorithmic and hardware optimizations","volume-title":"Proc. 59th ACM\/IEEE Design Autom. Conf.","author":"Fan"},{"key":"ref17","first-page":"2951","article-title":"Practical Bayesian optimization of machine learning algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Snoek"},{"key":"ref18","first-page":"2016","article-title":"Neural architecture search with Bayesian optimisation and optimal transport","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Kandasamy"},{"key":"ref19","first-page":"8620","article-title":"Dense for the price of sparse: Improved performance of sparsely initialized networks via a subspace offset","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Price"},{"key":"ref20","first-page":"10542","article-title":"Why random pruning is all we need to start sparse","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Gadhikar"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref22","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref23","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref24","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Krizhevsky"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.l803.03635"},{"key":"ref27","first-page":"2498","article-title":"Variational dropout sparsifies deep neural networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Molchanov"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11368859.pdf?arnumber=11368859","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:56:40Z","timestamp":1770843400000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11368859\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3659474","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}