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Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch.
Evolutionary principles in self-referential learning
Jurgen Schmidhuber · 1987
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Fixed-weight networks can learn
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Multitask learning
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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One shot learning of simple visual concepts
Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum · 2011
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Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
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Learning with partially absorbing random walks
Xiao-Ming Wu, Zhenguo Li, Anthony M So, John Wright, and Shih-Fu Chang · 2012
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2016
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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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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, and Daan Wierstra · 2016
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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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Trust region policy optimization
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