{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:37:03Z","timestamp":1778258223375,"version":"3.51.4"},"reference-count":61,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP180103232"],"award-info":[{"award-number":["DP180103232"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["CE140100016"],"award-info":[{"award-number":["CE140100016"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["FT190100525"],"award-info":[{"award-number":["FT190100525"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,1,1]]},"DOI":"10.1109\/tpami.2022.3147798","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T20:46:13Z","timestamp":1643748373000},"page":"841-851","source":"Crossref","is-referenced-by-count":10,"title":["PAC-Bayes Meta-Learning With Implicit Task-Specific Posteriors"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2672-6291","authenticated-orcid":false,"given":"Cuong","family":"Nguyen","sequence":"first","affiliation":[{"name":"Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-0848","authenticated-orcid":false,"given":"Thanh-Toan","family":"Do","sequence":"additional","affiliation":[{"name":"Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5571-6220","authenticated-orcid":false,"given":"Gustavo","family":"Carneiro","sequence":"additional","affiliation":[{"name":"Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/307400.307435"},{"key":"ref38","first-page":"3981","article-title":"Learning to learn by gradient descent by gradient descent","author":"andrychowicz","year":"2016","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"ref32","first-page":"5917","article-title":"Adaptive gradient-based meta-learning methods","author":"khodak","year":"2019","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref31","article-title":"The advantage of conditional meta-learning for biased regularization and fine tuning","author":"denevi","year":"2020","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref30","first-page":"1","article-title":"Online-within-online meta-learning","author":"denevi","year":"2019","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref37","first-page":"3630","article-title":"Matching networks for one shot learning","author":"vinyals","year":"2016","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref36","article-title":"Meta-SGD: Learning to learn quickly for few-shot learning","author":"li","year":"2017"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093536"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1613\/jair.731"},{"key":"ref60","article-title":"Hypernetworks","author":"ha","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref28","first-page":"10 190","article-title":"Learning to learn around a common mean","author":"denevi","year":"2018","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref27","first-page":"1","article-title":"The benefit of multitask representation learning","volume":"17","author":"maurer","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref29","first-page":"1566","article-title":"Learning-to-learn stochastic gradient descent with biased regularization","author":"denevi","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref2","first-page":"1","article-title":"To transfer or not to transfer","author":"rosenstein","year":"2005","journal-title":"Proc Conf Neural Informat Process Syst Workshop Transfer Learn"},{"key":"ref1","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref20","first-page":"7343","article-title":"Bayesian model-agnostic meta-learning","author":"yoon","year":"2018","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref22","first-page":"205","article-title":"Meta-learning by adjusting priors based on extended PAC-Bayes theory","author":"amit","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref21","first-page":"991","article-title":"A PAC-Bayesian bound for lifelong learning","author":"pentina","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref24","article-title":"On first-order meta-learning algorithms","author":"nichol","year":"2018"},{"key":"ref23","first-page":"543","article-title":"How important is the train-validation split in meta-learning?","author":"bai","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","first-page":"967","article-title":"Algorithmic stability and meta-learning","volume":"6","author":"maurer","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref25","first-page":"261","article-title":"Regret bounds for lifelong learning","author":"alquier","year":"2017","journal-title":"Proc Conf Artif Intell Statist"},{"key":"ref50","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref51","article-title":"Meta-learning for semi-supervised few-shot classification","author":"ren","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref57","article-title":"Transductive propagation network for few-shot learning","author":"liu","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref56","first-page":"3825","article-title":"LGM-Net: Learning to generate matching networks for few-shot learning","author":"li","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref55","first-page":"719","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","author":"oreshkin","year":"2018","journal-title":"Proc Conf Neural Informat Process Syst"},{"key":"ref54","first-page":"3661","article-title":"Rapid adaptation with conditionally shifted neurons","author":"munkhdalai","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref53","article-title":"A simple neural attentive meta-learner","author":"mishra","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref10","article-title":"Optimization as a model for few-shot learning","author":"ravi","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref11","first-page":"2554","article-title":"Meta networks","author":"munkhdalai","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref40","author":"catoni","year":"2004","journal-title":"Statistical Learning Theory and Stochastic Optimization Ecole D’Eté de Probabilités de Saint-Flour XXXI-2001"},{"key":"ref12","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref13","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","first-page":"2371","article-title":"MetaGAN: An adversarial approach to few-shot learning","author":"zhang","year":"2018","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref15","article-title":"Meta-learning with latent embedding optimization","author":"rusu","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","article-title":"Recasting gradient-based meta-learning as hierarchical Bayes","author":"grant","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref17","article-title":"Amortized Bayesian meta-learning","author":"ravi","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref18","article-title":"Meta-learning probabilistic inference for prediction","author":"gordon","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref19","first-page":"9537","article-title":"Probabilistic model-agnostic meta-learning","author":"finn","year":"2018","journal-title":"Proc Conf Neural Informat Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref5","first-page":"234","article-title":"RecNorm: Simultaneous normalisation and classification applied to speech recognition","author":"bridle","year":"1991","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5529-2"},{"key":"ref7","article-title":"Evolutionary principles in self-referential learning (on learning how to learn: The meta-meta-... hook)","author":"schmidhuber","year":"1987"},{"key":"ref49","first-page":"5897","article-title":"Distribution calibration for regression","author":"song","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref9","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","author":"santoro","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143855"},{"key":"ref45","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref48","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref47","article-title":"beta-VAE: Learn. basic visual concepts with a constrained variational framework","author":"higgins","year":"2016","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref42","first-page":"8374","article-title":"On the properties of variational approximations of Gibbs posteriors","volume":"17","author":"alquier","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref41","first-page":"1884","article-title":"PAC-Bayesian theory meets Bayesian inference","author":"germain","year":"2016","journal-title":"Proc Adv Neural Informat Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1984.tb01290.x"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5690-0"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9970415\/09699417.pdf?arnumber=9699417","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:11:32Z","timestamp":1672081892000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9699417\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":61,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3147798","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,1]]}}}