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Compression and computational efficiency in deep learning have become a problem of great significance.
Scale mixing of symmetric distributions with zero means
E. Beale, C. Mallows, et al · 1959
Earlier work this paper cites.
Scale mixtures of normal distributions
D. F. Andrews and C. L. Mallows · 1974
Earlier work this paper cites.
Modeling by shortest data description
J. Rissanen · 1978
Earlier work this paper cites.
Stochastic complexity and modeling
J. Rissanen · 1986
Earlier work this paper cites.
A mean field theory learning algorithm for neural networks
C. Peterson · 1987
Earlier work this paper cites.
Bayesian variable selection in linear regression
T. J. Mitchell and J. J. Beauchamp · 1988
Earlier work this paper cites.
Optimal brain damage
Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel · 1989
Earlier work this paper cites.
Classification by minimum-message-length inference
C. S. Wallace · 1990
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
G. E. Hinton and D. Van Camp · 1993
Earlier work this paper cites.
Probable networks and plausible predictions—a review of practical bayesian methods for supervised neural networks
D. J. MacKay · 1995
Earlier work this paper cites.
Bayesian learning for neural networks
R. M. Neal · 1995
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
Earlier work this paper cites.
Adaptive sparseness using jeffreys’ prior
M. A. Figueiredo · 2002
Earlier work this paper cites.
Note relevance determination
N. D. Lawrence · 2002
Earlier work this paper cites.
Variational learning and bits-back coding: an information-theoretic view to bayesian learning
A. Honkela and H. Valpola · 2004
Earlier work this paper cites.
The minimum description length principle
P. D. Grünwald · 2007
Earlier work this paper cites.
A general framework for the parametrization of hierarchical models
O. Papaspiliopoulos, G. O. Roberts, and M. Sköld · 2007
Earlier work this paper cites.
Ieee standard for floating-point arithmetic
M. Sites · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
The horseshoe estimator for sparse signals
C. M. Carvalho, N. G. Polson, and J. G. Scott · 2010
Earlier work this paper cites.
Generalized beta mixtures of gaussians
A. Armagan, M. Clyde, and D. B. Dunson · 2011
Earlier work this paper cites.
Practical variational inference for neural networks
A. Graves · 2011
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
Earlier work this paper cites.
Predicting parameters in deep learning
M. Denil, B. Shakibi, L. Dinh, N. de Freitas, et al · 2013
Earlier work this paper cites.
Do deep nets really need to be deep?
J. Ba and R. Caruana · 2014
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
M. Courbariaux, J.-P. David, and Y. Bengio · 2014
Cited alongside, same era.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
Cited alongside, same era.
Mean field variational bayes for continuous sparse signal shrinkage: pitfalls and remedies
S. E. Neville, J. T. Ormerod, M. Wand, et al · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Cited alongside, same era.
Weight uncertainty in neural networks
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
Later among the works it cites.
Bayesian sparsity for intractable distributions
J. B. Ingraham and D. S. Marks · 2016
Later among the works it cites.
Overcoming challenges in fixed point training of deep convolutional networks
D. D. Lin and S. S. Talathi · 2016
Later among the works it cites.
Deep neural networks are robust to weight binarization and other non-linear distortions
P. Merolla, R. Appuswamy, J. Arthur, S. K. Esser, and D. Modha · 2016
Later among the works it cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Later among the works it cites.
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W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen · 2015
Cited alongside, same era.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Cited alongside, same era.
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Y. Gong, L. Liu, M. Yang, and L. Bourdev · 2015
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Later among the works it cites.
Ladder variational autoencoders
C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther · 2016
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S. Srinivas and R. V. Babu · 2016
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Accelerating deep convolutional networks using low-precision and sparsity
G. Venkatesh, E. Nurvitadhi, and D. Marr · 2016
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Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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S. Zagoruyko and N. Komodakis · 2016
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