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We propose to exploit {\em reconstruction} as a layer-local training signal for deep learning.
Learning processes in an asymmetric threshold network
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A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
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Learning representations by recirculation
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Modèles connexionistes de l’apprentissage
LeCun, Y. (1987) · 1987
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Generalization of back-propagation to recurrent neural networks
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Connectionist learning procedures
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Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, T.U. Münich
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Connectionist learning of belief networks
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Learning mixture models of spatial coherence
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Learning long-term dependencies with gradient descent is difficult
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The Helmholtz machine
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S. (1995) · 1995
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The wake-sleep algorithm for unsupervised neural networks
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995) · 1995
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Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
O’Reilly, R. C. (1996) · 1996
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Long short-term memory
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Temporal coherence, natural image sequences, and the visual cortex
Hurri, J. and Hyvärinen, A. (2003) · 2002
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Real-time computing without stable states: A new framework for neural computation based on perturbations
Maass, W., Natschlaeger, T., and Markram, H. (2002) · 2002
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Slow feature analysis: Unsupervised learning of invariances
Wiskott, L. and Sejnowski, T. (2002) · 2002
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Equivalence of backpropagation and contrastive hebbian learning in a layered network
Xie, X. and Seung, H. S. (2003) · 2003
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Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
Jaeger, H. and Haas, H. (2004) · 2004
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How are complex cell properties adapted to the statistics of natural stimuli?
Körding, K. P., Kayser, C., Einhäuser, W., and König, P. (2004) · 2004
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Non-local manifold Parzen windows
Bengio, Y., Larochelle, H., and Vincent, P. (2006) · 2005
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Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2007) · 2006
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y. (2006) · 2006
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How to do backpropagation in a brain
Hinton, G. E. (2007) · 2007
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Some extensions of score matching
Hyvärinen, A. (2007) · 2007
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Efficient learning of sparse representations with an energy-based model
Ranzato, M., Poultney, C., Chopra, S., and LeCun, Y. (2007) · 2007
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Training Recurrent Neural Networks
Sutskever, I. (2012) · 2012
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What regularized auto-encoders learn from the data generating distribution
Alain, G. and Bengio, Y. (2013) · 2013
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Deep learning of representations: Looking forward
Bengio, Y. (2013) · 2013
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Stochastic ratio matching of RBMs for sparse high-dimensional inputs
Dauphin, Y. and Bengio, Y. (2013) · 2013
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Knowledge matters: Importance of prior information for optimization
Gulcehre, C. and Bengio, Y. (2013) · 2013
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A hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models
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Deep narrow sigmoid belief networks are universal approximators
Sutskever, I. and Hinton, G. E. (2008) · 2008
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008) · 2008
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Learning deep architectures for AI
Bengio, Y. (2009) · 2009
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Learning transformational invariants from natural movies
Cadieu, C. and Olshausen, B. (2009) · 2009
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Deep Boltzmann machines
Salakhutdinov, R. and Hinton, G. (2009) · 2009
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Hanuschkin, A., Ganguli, S., and Hahnloser, R. (2013) · 2013
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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y. (2013) · 2013
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Smart decisions by small adjustments: iterating denoising autoencoders
Bahdanau, D. and Jaeger, H. (2014) · 2014
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Deep generative stochastic networks trainable by backprop
Bengio, Y., Thibodeau-Laufer, E., and Yosinski, J. (2014) · 2014
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Bornschein, J. and Bengio, Y. (2014) · 2014
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Distributed optimization of deeply nested systems
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Evidence for a causal inverse model in an avian cortico-basal ganglia circuit
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Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Deep autoregressive networks
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Auto-encoding variational bayes
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Multimodal transitions for generative stochastic networks
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Stochastic backpropagation and approximate inference in deep generative models
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Multimodal learning with deep boltzmann machines
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