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This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.
The helmholtz machine
Dayan, Peter, Hinton, Geoffrey E, Neal, Radford M, and Zemel, Richard S · 1995
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Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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Learning to forget: Continual prediction with lstm
Gers, Felix A, Schmidhuber, Jürgen, and Cummins, Fred · 2000
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Reducing the dimensionality of data with neural networks
Hinton, Geoffrey E and Salakhutdinov, Ruslan R · 2006
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On the quantitative analysis of Deep Belief Networks
Salakhutdinov, Ruslan and Murray, Iain · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, Alex · 2009
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Evaluating probabilities under high-dimensional latent variable models
Murray, Iain and Salakhutdinov, Ruslan · 2009
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Deep boltzmann machines
Salakhutdinov, Ruslan and Hinton, Geoffrey E · 2009
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Learning to combine foveal glimpses with a third-order boltzmann machine
Larochelle, Hugo and Hinton, Geoffrey E · 2010
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The neural autoregressive distribution estimator
Larochelle, Hugo and Murray, Iain · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
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Multi-digit number recognition from street view imagery using deep convolutional neural networks
Goodfellow, Ian J, Bulatov, Yaroslav, Ibarz, Julian, Arnoud, Sacha, and Shet, Vinay · 2013
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Generating sequences with recurrent neural networks
Graves, Alex · 2013
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Learning generative models with visual attention
Tang, Yichuan, Srivastava, Nitish, and Salakhutdinov, Ruslan · 2013
Recurrent models of visual attention
Mnih, Volodymyr, Heess, Nicolas, Graves, Alex, et al · 2014
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Iterative neural autoregressive distribution estimator nade-k
Raiko, Tapani, Li, Yao, Cho, Kyunghyun, and Bengio, Yoshua · 2014
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Ranzato, Marc’Aurelio · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo J, Mohamed, Shakir, and Wierstra, Daan · 2014
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Markov chain monte carlo and variational inference: Bridging the gap
Salimans, Tim, Kingma, Diederik P, and Welling, Max · 2014
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Graves, Alex, Wayne, Greg, and Danihelka, Ivo · 2014
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Deep autoregressive networks
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Kingma, Diederik and Ba, Jimmy · 2014
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Sermanet, Pierre, Frome, Andrea, and Real, Esteban · 2014
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Sequence to sequence learning with neural networks
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Tieleman, Tijmen · 2014
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A deep and tractable density estimator
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A neural autoregressive approach to attention-based recognition
Zheng, Yin, Zemel, Richard S, Zhang, Yu-Jin, and Larochelle, Hugo · 2014
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