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Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.
Inadmissibility of the usual estimator for the mean of a multivariate normal distribution
Charles Stein · 1956
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Stein’s estimation rule and its competitors—an empirical Bayes approach
Bradley Efron and Carl Morris · 1973
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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Bayesian online changepoint detection
Ryan Prescott Adams and David JC MacKay · 2007
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On-line inference for multiple changepoint problems
Paul Fearnhead and Zhen Liu · 2007
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Change point detection and meta-bandits for online learning in dynamic environments
Cédric Hartland, Nicolas Baskiotis, Sylvain Gelly, Michèle Sebag, and Olivier Teytaud · 2007
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Empirical Bayesian change point detection
Ulrich Paquet · 2007
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Adaptive sequential Bayesian change point detection
Ryan Turner, Yunus Saatci, and Carl Edward Rasmussen · 2009
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Gaussian process change point models
Y Saatci, R Turner, and CE Rasmussen · 2010
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Bayesian online learning of the hazard rate in change-point problems
Robert C Wilson, Matthew R Nassar, and Joshua I Gold · 2010
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On upper-confidence bound policies for switching bandit problems
Aurélien Garivier and Eric Moulines · 2011
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Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
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Thompson sampling in switching environments with bayesian online change detection
Joseph Mellor and Jonathan Shapiro · 2013
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A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Lifelong machine learning
Zhiyuan Chen and Bing Liu · 2016
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Introduction to online convex optimization
Elad Hazan · 2016
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Recurrent switching linear dynamical systems
Scott W Linderman, Andrew C Miller, Ryan P Adams, David M Blei, Liam Paninski, and Matthew J Johnson · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Spatio-temporal bayesian on-line changepoint detection with model selection
Jeremias Knoblauch and Theodoros Damoulas · 2018
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Amortized bayesian meta-learning
Sachin Ravi and Alex Beatson · 2018
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, and Richard S Zemel · 2018
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Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
Carlos Riquelme, George Tucker, and Jasper Snoek · 2018
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Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
Closest in time.
A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2018
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Online meta-learning
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Task agnostic continual learning via meta learning
Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A Rusu, Yee Whye Teh, and Razvan Pascanu · 2019
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Meta-learning representations for continual learning
Khurram Javed and Martha White · 2019
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Online gradient-based mixtures for transfer modulation in meta-learning
Ghassen Jerfel, Erin Grant, Thomas L Griffiths, and Katherine Heller · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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Deep online learning via meta-learning: Continual adaptation for model-based RL
Anusha Nagabandi, Chelsea Finn, and Sergey Levine · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2019
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Fair meta-learning: Learning how to learn fairly
Dylan Slack, Sorelle Friedler, and Emile Givental · 2019
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Meta-learning with stochastic linear bandits
Leonardo Cella, Alessandro Lazaric, and Massimiliano Pontil · 2020
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