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Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning.
Using fast weights to deblur old memories
Hinton, Geoffrey E and Plaut, David C · 1987
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Schmidhuber, Jürgen · 1987
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Learning a synaptic learning rule
Bengio, Yoshua, Bengio, Samy, and Cloutier, Jocelyn · 1991
Earlier work this paper cites.
Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Schmidhuber, Jürgen · 1992
Earlier work this paper cites.
Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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Natural gradient works efficiently in learning
Amari, Shun-Ichi · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Lecun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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Learning to learn: Introduction and overview
Thrun, Sebastian and Pratt, Lorien · 1998
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
French, Robert M · 1999
Earlier work this paper cites.
Learning to learn using gradient descent
Hochreiter, Sepp, Younger, A. Steven, and Conwell, Peter R · 2001
Earlier work this paper cites.
Mirror descent and nonlinear projected subgradient methods for convex optimization
Beck, Amir and Teboulle, Marc · 2003
Earlier work this paper cites.
Introduction to Smooth Manifolds
Lee, John M · 2003
Earlier work this paper cites.
Numerical optimization
Nocedal, Jorge and Wright, Stephen · 2006
Earlier work this paper cites.
Methods of information geometry , volume 191
Amari, Shun-ichi and Nagaoka, Hiroshi · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
Earlier work this paper cites.
Deep learning via hessian-free optimization
Martens, James · 2010
Earlier work this paper cites.
One shot learning of simple visual concepts
Lake, Brenden, Salakhutdinov, Ruslan, Gross, Jason, and Tenenbaum, Joshua · 2011
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Sutskever, Ilya, Martens, James, Dahl, George, and Hinton, Geoffrey · 2013
Earlier work this paper cites.
Revisiting natural gradient for deep networks
Pascanu, Razvan and Bengio, Yoshua · 2014
Earlier work this paper cites.
Natural neural networks
Desjardins, Guillaume, Simonyan, Karen, Pascanu, Razvan, and kavukcuoglu, koray · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, Diederik P. and Ba, Jimmy · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Lake, Brenden M., Salakhutdinov, Ruslan, and Tenenbaum, Joshua B · 2015
Earlier work this paper cites.
Optimizing neural networks with kronecker-factored approximate curvature
Martens, James and Grosse, Roger · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Andrychowicz, Marcin, Denil, Misha, Gomez, Sergio, Hoffman, Matthew W, Pfau, David, Schaul, Tom, Shillingford, Brendan, and De Freitas, Nando · 2016
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Using fast weights to attend to the recent past
Ba, Jimmy, Hinton, Geoffrey E, Mnih, Volodymyr, Leibo, Joel Z, and Ionescu, Catalin · 2016
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Learning feed-forward one-shot learners
Bertinetto, Luca, Henriques, João F, Valmadre, Jack, Torr, Philip, and Vedaldi, Andrea · 2016
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A stochastic quasi-newton method for large-scale optimization
Byrd, R., Hansen, S., Nocedal, J., and Singer, Y · 2016
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Hypernetworks
Ha, David, Dai, Andrew M., and Le, Quoc V · 2016
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Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
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Uncertainty in multitask transfer learning
Lacoste, Alexandre, Oreshkin, Boris, Chung, Wonchang, Boquet, Thomas, Rostamzadeh, Negar, and Krueger, David · 2018
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Meta-Learning with Adaptive Layerwise Metric and Subspace
Lee, Yoonho and Choi, Seungjin · 2018
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Differentiable plasticity: training plastic neural networks with backpropagation
Miconi, Thomas, Clune, Jeff, and Stanley, Kenneth O · 2018
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A Simple Neural Attentive Meta-Learner
Mishra, Nikhil, Rohaninejad, Mostafa, Chen, Xi, and Abbeel, Pieter · 2018
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Learning rapid-temporal adaptations
Munkhdalai, Tsendsuren, Yuan, Xingdi, Mehri, Soroush, Wang, Tong, and Trischler, Adam · 2018
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Learning to optimize
Li, Ke and Malik, Jitendra · 2016
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Learning without forgetting
Li, Zhizhong and Hoiem, Derek · 2016
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Optimization as a model for few-shot learning
Ravi, Sachin and Larochelle, Hugo · 2016
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Matching Networks for One Shot Learning
Vinyals, Oriol, Blundell, Charles, Lillicrap, Timothy, Kavukcuoglu, Koray, and Wierstra, Daan · 2016
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Learning to reinforcement learn
Wang, Jane X., Kurth-Nelson, Zeb, Tirumala, Dhruva, Soyer, Hubert, Leibo, Joel Z., Munos, Rémi, Blundell, Charles, Kumaran, Dharshan, and Botvinick, Matthew · 2016
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Universal representations: The missing link between faces, text, planktons, and cat breeds
Bilen, Hakan and Vedaldi, Andrea · 2017
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Nichol, Alex, Achiam, Joshua, and Schulman, John · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, Boris N, Lacoste, Alexandre, and Rodriguez, Paul · 2018
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Film: Visual reasoning with a general conditioning layer
Perez, Ethan, Strub, Florian, De Vries, Harm, Dumoulin, Vincent, and Courville, Aaron · 2018
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Few-shot image recognition by predicting parameters from activations
Qiao, Siyuan, Liu, Chenxi, Shen, Wei, and Yuille, Alan L · 2018
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Meta-learning for semi-supervised few-shot classification
Ren, Mengye, Triantafillou, Eleni, Ravi, Sachin, Snell, Jake, Swersky, Kevin, Tenenbaum, Joshua B., Larochelle, Hugo, and Zemel, Richard S · 2018
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Understanding short-horizon bias in stochastic meta-optimization
Wu, Yuhuai, Ren, Mengye, Liao, Renjie, and Grosse, Roger B · 2018
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Deep meta-learning: Learning to learn in the concept space
Zhou, Fengwei, Wu, Bin, and Li, Zhenguo · 2018
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How to train your MAML
Antoniou, Antreas, Edwards, Harrison, and Storkey, Amos J · 2019
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Transferring knowledge across learning processes
Flennerhag, Sebastian, Moreno, Pablo G., Lawrence, Neil D., and Damianou, Andreas · 2019
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Meta-learning representations for continual learning
Javed, Khurram and White, Martha · 2019
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Meta-learning with differentiable convex optimization
Lee, Kwonjoon, Maji, Subhransu, Ravichandran, Avinash, and Soatto, Stefano · 2019
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Mendonca, Russell, Gupta, Abhishek, Kralev, Rosen, Abbeel, Pieter, Levine, Sergey, and Finn, Chelsea · 2019
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Meta-learning update rules for unsupervised representation learning
Metz, Luke, Maheswaranathan, Niru, Cheung, Brian, and Sohl-Dickstein, Jascha · 2019
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Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
Miconi, Thomas, Clune, Jeff, and Stanley, Kenneth O · 2019
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Park, Eunbyung and Oliva, Junier B · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Rakelly, Kate, Zhou, Aurick, Quillen, Deirdre, Finn, Chelsea, and Levine, Sergey · 2019
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Meta-learning with latent embedding optimization
Rusu, Andrei A., Rao, Dushyant, Sygnowski, Jakub, Vinyals, Oriol, Pascanu, Razvan, Osindero, Simon, and Hadsell, Raia · 2019
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Fast Context Adaptation via Meta-Learning
Zintgraf, Luisa M., Shiarlis, Kyriacos, Kurin, Vitaly, Hofmann, Katja, and Whiteson, Shimon · 2019
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