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Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently.
Evolutionary principles in self-referential learning
Jurgen Schmidhuber · 1987
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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On the approximate realization of continuous mappings by neural networks
Ken-Ichi Funahashi · 1989
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1990
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On the optimization of a synaptic learning rule
Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gecsei · 1992
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Fast learning for problem classes using knowledge based network initialization
Michael Husken and Christian Goerick · 2000
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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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Hypernetworks
David Ha, Andrew Dai, and Quoc V Le · 2017
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Learning to generalize: Meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Neural network with unbounded activation functions is universal approximator
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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
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine
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