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We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning.
“Evolutionary principles in self-referential learning (On learning how to learn: the meta-meta-… hook)”, 1987
J“”urgen Schmidhuber · 1987
Earlier work this paper cites.
“Recnorm: Simultaneous normalisation and classification applied to speech recognition”
John Bridle and Stephen Cox · 1991
Earlier work this paper cites.
“Meta-neural networks that learn by learning”
Devang Naik and RJ Mammone · 1992
Earlier work this paper cites.
“Multitask learning”
Rich Caruana · 1997
Earlier work this paper cites.
“Learning to learn”
Sebastian Thrun and Lorien Pratt · 1998
Earlier work this paper cites.
“A model of inductive bias learning”
Jonathan Baxter · 2000
Earlier work this paper cites.
“To transfer or not to transfer”
Michael Rosenstein, Zvika Marx, Leslie Kaelbling and Thomas Dietterich · 2005
Earlier work this paper cites.
“One-shot learning of object categories”
Li Fei-Fei, Rob Fergus and Pietro Perona · 2006
Earlier work this paper cites.
“ImageNet: A Large-Scale Hierarchical Image Database”
J. Deng et al · 2009
Earlier work this paper cites.
“Understanding the difficulty of training deep feedforward neural networks”
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
“A theory of learning from different domains”
Shai Ben-David et al · 2010
Earlier work this paper cites.
“Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups”
Geoffrey Hinton et al · 2012
Earlier work this paper cites.
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Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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Diederik Kingma and Max Welling · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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