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Back-propagation has been the workhorse of recent successes of deep learning but it relies on infinitesimal effects (partial derivatives) in order to perform credit assignment.
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Bengio, Y.: Estimating or propagating gradients through stochastic neurons · 2013
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Tech. rep., arXiv:1407.7906 (2014)
Bengio, Y.: How auto-encoders could provide credit assignment in deep networks via target propagation · 2014
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In: ICML’2014 (2014)
Bengio, Y., Thibodeau-Laufer, E., Yosinski, J.: Deep generative stochastic networks trainable by backprop · 2014
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In: AISTATS’2014, JMLR W&CP. vol. 33, pp. 10–19 (2014)
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NIPS Deep Learning Workshop 2014 (2014)
Raiko, T., Berglund, M., Alain, G., Dinh, L.: Techniques for learning binary stochastic feedforward neural networks · 2014
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Tech. rep., arXiv:1409.3215 (2014)
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Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation · 2013
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ICML’2013 Workshop on Challenges in Representation Learning (2013)
Tang, Y., Salakhutdinov, R.: A new learning algorithm for stochastic feedforward neural nets · 2013
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Under review on International Conference on Learning Representations (2015)
Konda, K., Memisevic, R., Krueger, D.: Zero-bias autoencoders and the benefits of co-adapting features · 2015
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