Dropout Inference in Bayesian Neural Networks with Alpha-divergences
暂无分享,去创建一个
[1] E. Hellinger,et al. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. , 1909 .
[2] A. Rényi. On Measures of Entropy and Information , 1961 .
[3] Shun-ichi Amari,et al. Differential-geometrical methods in statistics , 1985 .
[4] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[5] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[6] Yann LeCun,et al. Transforming Neural-Net Output Levels to Probability Distributions , 1990, NIPS.
[7] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[8] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[9] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[10] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[11] Charles M. Bishop,et al. Ensemble learning in Bayesian neural networks , 1998 .
[12] Tom Minka,et al. Expectation Propagation for approximate Bayesian inference , 2001, UAI.
[13] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[14] Thomas P. Minka,et al. Divergence measures and message passing , 2005 .
[15] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[16] Fu Jie Huang,et al. A Tutorial on Energy-Based Learning , 2006 .
[17] Lukás Burget,et al. Recurrent neural network based language model , 2010, INTERSPEECH.
[18] Alex Graves,et al. Practical Variational Inference for Neural Networks , 2011, NIPS.
[19] Richard E. Turner,et al. Two problems with variational expectation maximisation for time-series models , 2011 .
[20] Evgueni A. Haroutunian,et al. Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.
[21] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[22] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[23] Hermann Ney,et al. LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.
[24] Yann LeCun,et al. Regularization of Neural Networks using DropConnect , 2013, ICML.
[25] Phil Blunsom,et al. Recurrent Continuous Translation Models , 2013, EMNLP.
[26] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[27] Peter Harremoës,et al. Rényi Divergence and Kullback-Leibler Divergence , 2012, IEEE Transactions on Information Theory.
[28] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[29] G. Crooks. On Measures of Entropy and Information , 2015 .
[30] Zoubin Ghahramani,et al. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference , 2015, ArXiv.
[31] Julien Cornebise,et al. Weight Uncertainty in Neural Network , 2015, ICML.
[32] Ryan P. Adams,et al. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks , 2015, ICML.
[33] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[34] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[35] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Richard E. Turner,et al. Stochastic Expectation Propagation , 2015, NIPS.
[37] Roberto Cipolla,et al. Modelling uncertainty in deep learning for camera relocalization , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[38] Rico Sennrich,et al. Edinburgh Neural Machine Translation Systems for WMT 16 , 2016, WMT.
[39] Yarin Gal,et al. Uncertainty in Deep Learning , 2016 .
[40] Richard E. Turner,et al. Rényi Divergence Variational Inference , 2016, NIPS.
[41] Daniel Hernández-Lobato,et al. Deep Gaussian Processes for Regression using Approximate Expectation Propagation , 2016, ICML.
[42] Xiao Yang,et al. Fast Predictive Image Registration , 2016, LABELS/DLMIA@MICCAI.
[43] Richard E. Turner,et al. Black-box α-divergence minimization , 2016, ICML 2016.
[44] Daniel Hernández-Lobato,et al. Black-Box Alpha Divergence Minimization , 2015, ICML.
[45] John Schulman,et al. Concrete Problems in AI Safety , 2016, ArXiv.
[46] Patrick D. McDaniel,et al. Cleverhans V0.1: an Adversarial Machine Learning Library , 2016, ArXiv.
[47] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[48] Finale Doshi-Velez,et al. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks , 2016, ICLR.
[49] Roberto Cipolla,et al. Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.
[50] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).