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Deep dynamic generative models are developed to learn sequential dependencies in time-series data.
Mathematical description of linear dynamical systems
R. Kalman · 1963
Earlier work this paper cites.
An introduction to hidden markov models
L. Rabiner and B. Juang · 1986
Earlier work this paper cites.
Backpropagation through time: what it does and how to do it
P. Werbos · 1990
Earlier work this paper cites.
Connectionist learning of belief networks
R. Neal · 1992
Earlier work this paper cites.
The “wake-sleep” algorithm for unsupervised neural networks
G. Hinton, P. Dayan, B. Frey, and R. Neal · 1995
Earlier work this paper cites.
The helmholtz machine through time
G. Hinton, P. Dayan, A. To, and R. Neal · 1995
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
G. Hinton · 2002
Earlier work this paper cites.
Modeling human motion using binary latent variables
G. Taylor, G. Hinton, and S. Roweis · 2006
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
G. Hinton, S. Osindero, and Y. Teh · 2006
Earlier work this paper cites.
Learning multilevel distributed representations for high-dimensional sequences
I. Sutskever and G. Hinton · 2007
Earlier work this paper cites.
The recurrent temporal restricted boltzmann machine
I. Sutskever, G. Hinton, and G. Taylor · 2009
Earlier work this paper cites.
Replicated softmax: an undirected topic model
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Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
Neural variational inference and learning in belief networks
A. Mnih and K. Gregor · 2014
Later among the works it cites.
Stochastic backpropagation and approximate inference in deep generative models
D. Rezende, S. Mohamed, and D. Wierstra · 2014
Later among the works it cites.
Learning stochastic recurrent networks
J. Bayer and C. Osendorfer · 2014
Later among the works it cites.
Variational recurrent auto-encoders
O. Fabius, J. R. van Amersfoort, and D. P. Kingma · 2014
Later among the works it cites.
Dynamic rank factor model for text streams
S. Han, L. Du, E. Salazar, and L. Carin · 2014
Later among the works it cites.
Learning deep sigmoid belief networks with data augmentation
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On the difficulty of training recurrent neural networks
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Generating sequences with recurrent neural networks
A. Graves · 2013
Cited alongside, same era.
Structured recurrent temporal restricted boltzmann machines
R. Mittelman, B. Kuipers, S. Savarese, and H. Lee · 2014
Cited alongside, same era.
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