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Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks.
Exact distribution of the max/min of two gaussian random variables
Nadarajah, S. and Kotz, S. (2008) · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y. (2009) · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
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Neural networks for machine learning
Hinton, G. (2012) · 2012
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Stochastic pooling for regularization of deep convolutional neural networks
Zeiler, M. D. and Fergus, R. (2013) · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights
Soudry, D., Hubara, I., and Meir, R. (2014) · 2014
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Han, S., Mao, H., and Dally, W. J. (2015) · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M. (2015) · 2015
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Binarized neural networks
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y. (2016) · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B. (2016) · 2016
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Towards accurate binary convolutional neural network
Lin, X., Zhao, C., and Pan, W. (2017) · 2017
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Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M. (2017) · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017) · 2017
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Soft weight-sharing for neural network compression
Ullrich, K., Meeds, E., and Welling, M. (2017) · 2017
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Variational network quantization
Achterhold, J., Koehler, J. M., Schmeink, A., and Genewein, T. (2018) · 2018
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Attacking binarized neural networks
Galloway, A., Taylor, G. W., and Moussa, M. (2018) · 2018
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W. (2016) · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A. (2016) · 2016
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Zagoruyko, S. and Komodakis, N. (2016) · 2016
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Selective classification for deep neural networks
Geifman, Y. and El-Yaniv, R. (2017) · 2017
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Training wide residual networks for deployment using a single bit for each weight
McDonnell, M. D. (2018) · 2018
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Learning discrete weights using the local reparameterization trick
Shayer, O., Levi, D., and Fetaya, E. (2018) · 2018
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