{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T20:45:00Z","timestamp":1772225100898,"version":"3.50.1"},"reference-count":36,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,10]]},"DOI":"10.1109\/icpr48806.2021.9412010","type":"proceedings-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T02:15:54Z","timestamp":1620267354000},"page":"4161-4168","source":"Crossref","is-referenced-by-count":21,"title":["Meta Learning via Learned Loss"],"prefix":"10.1109","author":[{"given":"Sarah","family":"Bechtle","sequence":"first","affiliation":[]},{"given":"Artem","family":"Molchanov","sequence":"additional","affiliation":[]},{"given":"Yevgen","family":"Chebotar","sequence":"additional","affiliation":[]},{"given":"Edward","family":"Grefenstette","sequence":"additional","affiliation":[]},{"given":"Ludovic","family":"Righetti","sequence":"additional","affiliation":[]},{"given":"Gaurav","family":"Sukhatme","sequence":"additional","affiliation":[]},{"given":"Franziska","family":"Meier","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","first-page":"2402","article-title":"Meta-gradient reinforcement learning","author":"xu","year":"2018","journal-title":"NeurIPS"},{"key":"ref32","first-page":"6467","article-title":"Learning to teach with dynamic loss functions","author":"wu","year":"2018","journal-title":"NeurIPS"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref30","article-title":"Learning to learn","author":"thrun","year":"2012","journal-title":"Business Media"},{"key":"ref36","author":"zou","year":"2019","journal-title":"Reward shaping via meta-learning"},{"key":"ref35","author":"yu","year":"2018","journal-title":"One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning"},{"key":"ref34","author":"yadan","year":"2019","journal-title":"A framework for elegantly configuring complex applications"},{"key":"ref10","first-page":"5302","article-title":"Meta-reinforcement learning of structured exploration strategies","author":"gupta","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref11","first-page":"5405","article-title":"Evolved policy gradients","author":"houthooft","year":"2018","journal-title":"NeurIPS"},{"key":"ref12","article-title":"Unsupervised learning via meta-learning","volume":"abs 1810 2334","author":"hsu","year":"2018","journal-title":"CoRR"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref14","author":"li","year":"2016","journal-title":"Learning to Optimize[J]"},{"key":"ref15","author":"lutter","year":"2019","journal-title":"Deep lagrangian networks Using physics as model prior for deep learning"},{"key":"ref16","first-page":"2113","article-title":"Gradient-based hyperparameter optimization through reversible learning","author":"maclaurin","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460625"},{"key":"ref18","author":"mendonca","year":"2019","journal-title":"Guided meta-policy search"},{"key":"ref19","article-title":"Learning unsupervised learning rules","author":"metz","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref28","article-title":"Policy gradient methods for reinforcement learning with function approximation","author":"sutton","year":"2000","journal-title":"NeurIPS"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30301-5_60"},{"key":"ref27","author":"sung","year":"2017","journal-title":"Learning to learn Meta-critic networks for sample efficient learning"},{"key":"ref3","article-title":"Learning a synaptic learning rule","author":"bengio","year":"1990","journal-title":"Technical Report 751 Département d'Informatique et de Recherche Opérationelle Université de Montréal Montreal Canada"},{"key":"ref6","author":"featherstone","year":"2014","journal-title":"Rigid Body Dynamics Algorithms"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907001"},{"key":"ref5","article-title":"R12: Fast reinforcement learning via slow reinforcement learning","volume":"abs 1611 2779","author":"duan","year":"2016","journal-title":"CoRR"},{"key":"ref8","first-page":"1165","article-title":"Forward and reverse gradient-based hyperparameter optimization","author":"franceschi","year":"0","journal-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70"},{"key":"ref7","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"2017","journal-title":"ICML"},{"key":"ref2","first-page":"3981","article-title":"Learning to learn by gradient descent by gradient descent","author":"andrychowicz","year":"2016","journal-title":"NeurIPS"},{"key":"ref9","author":"grefenstette","year":"2019","journal-title":"Generalized inner loop meta-learning"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015430"},{"key":"ref20","author":"moore","year":"1990","journal-title":"Efficient memory-based learning for robot control"},{"key":"ref22","year":"2019","journal-title":"OpenAI Gym"},{"key":"ref21","first-page":"663","article-title":"Algorithms for inverse reinforcement learning","author":"ng","year":"2000","journal-title":"ICML"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.1.88"},{"key":"ref23","author":"parascandolo","year":"2017","journal-title":"Taming the Waves Sine as Activation Function in Deep Neural Networks"},{"key":"ref26","first-page":"3528","article-title":"Gradient estimation using stochastic computation graphs","author":"schulman","year":"2015","journal-title":"NeurIPS"},{"key":"ref25","article-title":"Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook","author":"schmidhuber","year":"1987","journal-title":"Institut für Informatik der Technischen Universität München"}],"event":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","location":"Milan, Italy","start":{"date-parts":[[2021,1,10]]},"end":{"date-parts":[[2021,1,15]]}},"container-title":["2020 25th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9411940\/9411911\/09412010.pdf?arnumber=9412010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:40:56Z","timestamp":1652197256000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412010\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412010","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}