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The field of few-shot learning has recently seen substantial advancements.
Evolutionary principles in self-referential learning
Jurgen Schmidhuber · 1987
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Learning many related tasks at the same time with backpropagation
Rich Caruana · 1995
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Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Pratt · 1998
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The cross-entropy method for combinatorial and continuous optimization
Reuven Rubinstein · 1999
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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A tutorial on the cross-entropy method
Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
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Using fast weights to attend to the recent past
Jimmy Ba, Geoffrey E Hinton, Volodymyr Mnih, Joel Z Leibo, and Catalin Ionescu · 2016
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Harrison Edwards and Amos Storkey · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Fractalnet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Ke Li and Jitendra Malik · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Smash: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2017
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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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Meta-sgd: Learning to learn quickly for few shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
Cited alongside, same era.
Learning to reinforcement learn
J. X Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Cited alongside, same era.
Later among the works it cites.
Tsendsuren Munkhdalai and Hong Yu · 2017
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Task-agnostic meta-learning for few-shot learning
Muhammad Abdullah Jamal, Guo-Jun Qi, and Mubarak Shah · 2018
Closest in time.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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How does batch normalization help optimization?(no, it is not about internal covariate shift)
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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