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Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions.
The” wake-sleep” algorithm for unsupervised neural networks
Geoffrey E Hinton, Peter Dayan, Brendan J Frey, and Radford M Neal · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Semiparametric latent factor models
Yee Whye Teh, Matthias Seeger, and Michael Jordan · 2005
Earlier work this paper cites.
Multi-task gaussian process prediction
Edwin V Bonilla, Kian M Chai, and Christopher Williams · 2008
Earlier work this paper cites.
Kernels for vector-valued functions: A review
Mauricio A Alvarez, Lorenzo Rosasco, Neil D Lawrence, et al · 2012
Earlier work this paper cites.
Supervised sequence labelling with recurrent neural networks
Alex Graves · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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One-shot generalization in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, and Daan Wierstra · 2016
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One-shot 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, Daan Wierstra, et al · 2016
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Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing · 2016
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Variational memory addressing in generative models
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Vfunc: a deep generative model for functions
Philip Bachman, Riashat Islam, Alessandro Sordoni, and Zafarali Ahmed · 2018
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Fast adaptation in generative models with generative matching networks
Sergey Bartunov and Dmitry P Vetrov · 2018
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Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
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The variational homoencoder: Learning to learn high capacity generative models from few examples
Luke B Hewitt, Maxwell I Nye, Andreea Gane, Tommi Jaakkola, and Joshua B Tenenbaum · 2018
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Jörg Bornschein, Andriy Mnih, Daniel Zoran, and Danilo Jimenez Rezende · 2017
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Efficient modeling of latent information in supervised learning using gaussian processes
Zhenwen Dai, Mauricio A Álvarez, and Neil Lawrence · 2017
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Towards a neural statistician
Harrison Edwards and Amos Storkey · 2017
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Few-shot autoregressive density estimation: Towards learning to learn distributions
Scott Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, SM Eslami, Danilo Rezende, Oriol Vinyals, and Nando de Freitas · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Conditional neural processes
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, and SM Ali Eslami
Cited in the paper.
Neural processes
Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J Rezende, SM Eslami, and Yee Whye Teh
Cited in the paper.
Ananya Kumar, SM Eslami, Danilo J Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, and Murray Shanahan · 2018
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Variational implicit processes
Chao Ma, Yingzhen Li, and José Miguel Hernández-Lobato · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, and Alexander Ku · 2018
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Learning models for visual 3d localization with implicit mapping
Dan Rosenbaum, Frederic Besse, Fabio Viola, Danilo J Rezende, and SM Eslami · 2018
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