{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:17:54Z","timestamp":1740133074567,"version":"3.37.3"},"reference-count":19,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Lett."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1109\/lcomm.2022.3208969","type":"journal-article","created":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T20:13:02Z","timestamp":1664827982000},"page":"372-376","source":"Crossref","is-referenced-by-count":0,"title":["Coded Distributed Gaussian Process Regression"],"prefix":"10.1109","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3001-8389","authenticated-orcid":false,"given":"Nikita","family":"Zeulin","sequence":"first","affiliation":[{"name":"Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5386-1061","authenticated-orcid":false,"given":"Olga","family":"Galinina","sequence":"additional","affiliation":[{"name":"Tampere University, Tampere, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4060-9406","authenticated-orcid":false,"given":"Nageen","family":"Himayat","sequence":"additional","affiliation":[{"name":"Intel Corporation, Santa Clara, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-3665","authenticated-orcid":false,"given":"Sergey","family":"Andreev","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Deep neural networks as Gaussian processes","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lee"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3040676"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2020.2990950"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2870160"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LAWP.2019.2934944"},{"key":"ref6","first-page":"2068","article-title":"Healing products of Gaussian process experts","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Cohen"},{"key":"ref7","first-page":"3131","article-title":"Generalized robust Bayesian committee machine for large-scale Gaussian process regression","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-017-9766-2"},{"key":"ref9","first-page":"1481","article-title":"Distributed Gaussian processes","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Deisenroth"},{"key":"ref10","article-title":"Generalized product of experts for automatic and principled fusion of Gaussian process predictions","author":"Cao","year":"2014","journal-title":"arXiv:1410.7827"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300014908"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036961"},{"key":"ref13","first-page":"1","article-title":"Random features for large-scale kernel machines","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"20","author":"Rahimi"},{"article-title":"Evaluation Gaussian processes other methods for non-linear regression","year":"1997","author":"Rasmussen","key":"ref14"},{"key":"ref15","first-page":"3011","article-title":"Gaussian processes for machine learning (GPML) toolbox","volume":"11","author":"Rasmussen","year":"2010","journal-title":"J. Mach. Learn. Res."},{"volume-title":"Study on Channel Model for Frequencies From 0.5 to 100 GHz","year":"2022","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/VTCSpring.2016.7503971"},{"volume-title":"The GFLOPS\/W of the Various Machines in the VMW Research Group","year":"2018","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"}],"container-title":["IEEE Communications Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4234\/10008392\/09908152.pdf?arnumber=9908152","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T22:47:52Z","timestamp":1705963672000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9908152\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":19,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/lcomm.2022.3208969","relation":{},"ISSN":["1089-7798","1558-2558","2373-7891"],"issn-type":[{"type":"print","value":"1089-7798"},{"type":"electronic","value":"1558-2558"},{"type":"electronic","value":"2373-7891"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}