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We study the behavior of untrained neural networks whose weights and biases are randomly distributed using mean field theory.
The problem of learning long-term dependencies in recurrent networks
Y Bengio, Paolo Frasconi, and P Simard · 1993
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At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks
Nils Bertschinger, Thomas Natschläger, and Robert A. Legenstein · 2005
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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Qualitatively characterizing neural network optimization problems
Ian J Goodfellow, Oriol Vinyals, and Andrew M Saxe · 2014
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Random feedback weights support learning in deep neural networks
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2014
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On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
A. M. Saxe, J. L. McClelland, and S. Ganguli · 2014
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Random walks: Training very deep nonlinear feed-forward networks with smart initialization
David Sussillo and LF Abbott · 2014
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The loss surfaces of multilayer networks
Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, and Yann LeCun · 2015
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Train faster, generalize better: Stability of stochastic gradient descent
Benjamin Recht, Moritz Hardt, and Yoram Singer · 2015
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A. Daniely, R. Frostig, and Y. Singer · 2016
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Exponential expressivity in deep neural networks through transient chaos
B. Poole, S. Lahiri, M. Raghu, J. Sohl-Dickstein, and S. Ganguli · 2016
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M. Raghu, B. Poole, J. Kleinberg, S. Ganguli, and J. Sohl-Dickstein · 2016
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