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We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models.
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 1901
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Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
Bryan Lim, Stefan Zohren, and Stephen Roberts · 1901
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Gautam Singh, Jaesik Yoon, Youngsung Son, and Sungjin Ahn · 1906
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Neural sequence chunkers
Jürgen H. Schmidhuber · 1991
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Learning complex, extended sequences using the principle of history compression
J. Schmidhuber · 1992
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Hierarchical recurrent neural networks for long-term dependencies
Salah El Hihi and Yoshua Bengio · 1996
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Input-output data sets for development and benchmarking in nonlinear identification
Torbjörn Wigren · 2010
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Stochastic processes: a survey of the mathematical theory , volume 23
John Lamperti · 2012
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Jan Koutnik, Klaus Greff, Faustino Gomez, and Juergen Schmidhuber · 2014
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Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, and S. M. Ali Eslami
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César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A. Barreto, and Neil D. Lawrence · 2015
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UCI machine learning repository - individual household electric power con- sumption data set, 2017., 2017
Dheeru Dua and Casey Graff · 2017
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The Sacred Infrastructure for Computational Research
Greff, Aaron Klein, Martin Chovanec, Frank Hutter, and Jürgen Schmidhuber · 2017
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Empirical Evaluation of Neural Process Objectives
Tuan Anh Le, Hyunjik Kim, Marta Garnelo, Dan Rosenbaum, Jonathan Schwarz, and Yee Whye Teh · 2018
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Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, and Yee Whye Teh
Cited in the paper.