{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:44:48Z","timestamp":1753602288510,"version":"3.37.3"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"NSF","award":["2212506"],"award-info":[{"award-number":["2212506"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tpami.2024.3381936","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T20:16:12Z","timestamp":1711484172000},"page":"6443-6453","source":"Crossref","is-referenced-by-count":3,"title":["Gaussian Process-Gated Hierarchical Mixtures of Experts"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9636-747X","authenticated-orcid":false,"given":"Yuhao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4613-5018","authenticated-orcid":false,"given":"Marzieh","family":"Ajirak","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7791-3199","authenticated-orcid":false,"given":"Petar M.","family":"Djuri\u0107","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1162\/neco.1994.6.2.181"},{"year":"2012","author":"Bishop","article-title":"Bayesian hierarchical mixtures of experts","key":"ref2"},{"key":"ref3","first-page":"21388","article-title":"Amortized variational inference for simple hierarchical models","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Agrawal"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1002\/widm.8"},{"volume-title":"C4. 5: Programs for Machine Learning","year":"2014","author":"Quinlan","key":"ref5"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1613\/jair.63"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1142\/S0218001405004125"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/CVPR.1992.223275"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/72.963795"},{"key":"ref10","first-page":"1819","article-title":"Soft decision trees","volume-title":"Proc. 21st Int. Conf. Pattern Recognit.","author":"Irsoy"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1007\/978-3-030-75765-6_12"},{"year":"2017","author":"Frosst","article-title":"Distilling a neural network into a soft decision tree","key":"ref12"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1007\/s11222-005-4787-7"},{"year":"2014","author":"Ng","article-title":"Hierarchical mixture-of-experts model for large-scale Gaussian process regression","key":"ref14"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1145\/1273496.1273557"},{"year":"2017","author":"Lee","article-title":"Deep neural networks as Gaussian processes","key":"ref16"},{"key":"ref17","first-page":"4697","article-title":"Bayesian deep learning and a probabilistic perspective of generalization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wilson"},{"key":"ref18","first-page":"9443","article-title":"Deep neural networks as point estimates for deep Gaussian processes","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Dutordoir"},{"key":"ref19","first-page":"3349","article-title":"The limitations of large width in neural networks: A deep Gaussian process perspective","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Pleiss"},{"volume-title":"Bayesian Learning for Neural Networks","year":"2012","author":"Neal","key":"ref20"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.5555\/1046920.1194909"},{"key":"ref22","first-page":"1177","article-title":"Random features for large-scale kernel machines","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Rahimi"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1007\/s11222-019-09886-w"},{"key":"ref24","first-page":"342","article-title":"Kernel methods for deep learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Cho"},{"key":"ref25","first-page":"655","article-title":"Improving the Gaussian process sparse spectrum approximation by representing uncertainty in frequency inputs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gal"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1198\/016214508000000689"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1016\/j.csda.2023.107858"},{"key":"ref28","first-page":"54","article-title":"GP-Tree: A Gaussian process classifier for few-shot incremental learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Achituve"},{"key":"ref29","first-page":"884","article-title":"Random feature expansions for deep Gaussian processes","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cutajar"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1109\/ICDAR.2003.1227801"},{"year":"1997","author":"Sch\u00f6lkopf","article-title":"Support vector learning","key":"ref31"},{"key":"ref32","first-page":"351","article-title":"Scalable variational Gaussian process classification","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Hensman"},{"year":"2016","author":"Krauth","article-title":"AutoGP: Exploring the capabilities and limitations of Gaussian process models","key":"ref33"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1109\/ICPR.2014.616"},{"key":"ref35","first-page":"2586","article-title":"Stochastic variational deep kernel learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wilson"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/10627928\/10480265-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10627928\/10480265.pdf?arnumber=10480265","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T17:46:31Z","timestamp":1723052791000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10480265\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":35,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3381936","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"},{"type":"electronic","value":"1939-3539"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}