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We present a novel approach for training deep neural networks in a Bayesian way.
Shun-Ichi A (1985) Differential-Geometrical Methods in Statistics. Springer
1985
Earlier work this paper cites.
Hornik K, Stinchcombe M, White H (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Elsevier Neural Networks
1990
Earlier work this paper cites.
Hinton GE, van Camp D (1993) Keeping the neural networks simple by minimizing the description length of the weights. In: Proceedings of the sixth annual conference on Computational learning theory
1993
Earlier work this paper cites.
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. IEEE
1998
Earlier work this paper cites.
Jordan MI, Ghahramani Z, Jaakkola TS, Saul LK (1999) An introduction to variational methods for graphical models. Machine Learning 37(2):183–233
1999
Earlier work this paper cites.
Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL (2006) Neural Probabilistic Language Models. Springer, in Innovations in Machine Learning
2006
Earlier work this paper cites.
Bishop CM (2006) Pattern Recognition and Machine Learning. Springer
2006
Earlier work this paper cites.
Hershey JR, Olsen PA (2007) Approximating the kullback leibler divergence between gaussian mixture models. ICASSP
2007
Earlier work this paper cites.
Graves A (2011) Practical variational inference for neural networks. NIPS
2011
Earlier work this paper cites.
Bengio Y (2012) Deep learning of representations for unsupervised and transfer learning. In: JMLR: Workshop and Conference Proceedings, vol 27, pp 17–37
2012
Earlier work this paper cites.
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. NIPS
2012
Earlier work this paper cites.
Wan L, Zeiler M, Zhang S, LeCun Y, Fergus R (2013) Regularization of neural networks using dropconnect. ICML
2013
Cited alongside, same era.
Arora S, Bhaskara A, Ge R, Ma T (2014) Provable bounds for learning some deep representations. In: Proceedings of the 31st International Conference on Machine Learning
2014
Cited alongside, same era.
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:14085093
2014
Cited alongside, same era.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning
2014
Cited alongside, same era.
Blundell C, Cornebise J, Kavukcuoglu K, Wierstra D (2015) Weight uncertainty in neural networks. ICML
2015
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, the Journal of the American Medical Association 316(22)
2016
Later among the works it cites.
Louizos C, Welling M (2016) Structured and efficient variational deep learning with matrix gaussian posteriors. arXiv:160304733 [statML]
2016
Later among the works it cites.
Banerjee K, Dinh TV, Levkova L (2017) Velocity estimation from monocular video for automotive applications using convolutional neural networks. In: IEEE Intelligent Vehicles Symposium
2017
Later among the works it cites.
Heaton J, Polson N, Witte J (2017) Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry 33(1)
2017
Later among the works it cites.
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Cited alongside, same era.
Hernandez-Lobato JM, Li Y, Rowland M, Hernandez-Lobato D, Bui T, Turner RE (2015) Black-box alpha divergence minimization. arXiv:151103243 [statML]
2015
Cited alongside, same era.
Kingma DP, Salimans T, Welling M (2015) Variational dropout and the local reparameterization trick. arXiv:150602557 [statML]
2015
Cited alongside, same era.
Blei DM, Kucukelbir A, McAuliffe JD (2016) Variational inference: A review for statisticians. arXiv:160100670 [statCO]
2016
Cited alongside, same era.
BVLC (2016) Caffe. https://github.com/BVLC/caffe
2016
Cited alongside, same era.
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press
2016
Cited alongside, same era.
Greenspan H, van Ginneken B, Summers RM (2016) Deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging 35(5)
2016
Cited alongside, same era.
Gal Y, Ghahramani Z (2016a) Bayesian convolutional neural networks with bernoulli approximate variational inference. ICLR
Cited in the paper.
Jozwik KM, Kriegeskorte N, Storrs KR, Mur M (2017) Deep convolutional neural networks outperform feature-based but not categorical models in explaining object similarity judgements. Frontiers in Psychology 8
2017
Later among the works it cites.
Li Y, Gal Y (2017) Dropout inference in bayesian neural networks with alpha-divergences. arXiv:170302914 [csLG]
2017
Later among the works it cites.
Liang S, Srikant R (2017) Why deep neural networks for function approximation? ICLR
2017
Later among the works it cites.
Rowan A (2017) Bayesian deep learning with edward (and a trick using dropout). PyData
2017
Later among the works it cites.
LeCun Y, Cortes C, Burges CJ (2018) The mnist database of handwritten digits. URL http://yann.lecun.com/exdb/mnist/
2018
Later among the works it cites.
Li X, Ding Q, Sun JQ (2018) Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety 172
2018
Later among the works it cites.