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The online meta-learning framework is designed for the continual lifelong learning setting.
“Online meta-learning,”
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine, · 1930
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“Meta-neural networks that learn by learning,”
Devang K Naik and RJ Mammone, · 1992
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Introductory lectures on convex optimization: A basic course
Y. Nesterov, · 2003
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Prediction, learning, and games
Nicolo Cesa-Bianchi and Gabor Lugosi, · 2006
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Learning to learn
Sebastian Thrun and Lorien Pratt, · 2012
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“Adam: A method for stochastic optimization,”
Diederik P. Kingma and Jimmy Ba, · 2014
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“Deep learning,”
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, · 2015
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“Human-level concept learning through probabilistic program induction,”
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum, · 2015
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“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
Sergey Ioffe and Christian Szegedy, · 2015
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“Mastering the game of go with deep neural networks and tree search,”
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al., · 2016
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“You only look once: Unified, real-time object detection,”
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi, · 2016
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“Deep speech 2: End-to-end speech recognition in English and Mandarin,”
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al., · 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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“Efficient regret minimization in non-convex games,”
Elad E Hazan, Karan Singh, and Cyril Zhang, · 2017
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“Optimization as a model for few-shot learning,”
Sachin Ravi and Hugo Larochelle, · 2017
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“Automatic differentiation in PyTorch,”
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer, · 2017
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“On first-order meta-learning algorithms,”
Alex Nichol, Joshua Achiam, and John Schulman, · 2018
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“How to train your MAML,”
Antreas Antoniou, Harrison Edwards, and Amos Storkey, · 2019
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“Adagrad stepsizes: sharp convergence over nonconvex landscapes,”
Rachel Ward, Xiaoxia Wu, and Leon Bottou, · 2019
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“Meta-learning with memory-augmented neural networks,”
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap, · 2016
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“Actor-mimic: Deep multitask and transfer reinforcement learning,”
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov, · 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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“On the convergence of stochastic gradient descent with adaptive stepsizes,”
Xiaoyu Li and Francesco Orabona, · 2019
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“Distribution-agnostic model-agnostic meta-learning,”
Liam Collins, Aryan Mokhtari, and Sanjay Shakkottai, · 2020
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