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Variational dropout (VD) is a generalization of Gaussian dropout, which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weights as well as dropout rate simultaneously.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Sparse bayesian learning and the relevance vector machine
Michael E Tipping · 2001
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Variational learning of clusters of undercomplete nonsymmetric independent components
Kwokleung Chan, Te-Won Lee, and Terrence J Sejnowski · 2002
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Bayesian blind deconvolution with general sparse image priors
S Derin Babacan, Rafael Molina, Minh N Do, and Aggelos K Katsaggelos · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Fast dropout training
Sida Wang and Christopher Manning · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
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Variational dropout and the local reparameterization trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
Cited alongside, same era.
Reweighted laplace prior based hyperspectral compressive sensing for unknown sparsity
Lei Zhang, Wei Wei, Yanning Zhang, Chunna Tian, and Fei Li · 2015
Cited alongside, same era.
Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Cited alongside, same era.
Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Cited alongside, same era.
Bayesian compression for deep learning
Christos Louizos, Karen Ullrich, and Max Welling · 2017
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Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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Structured bayesian pruning via log-normal multiplicative noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, and Dmitry P Vetrov · 2017
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Soft weight-sharing for neural network compression
Karen Ullrich, Edward Meeds, and Max Welling · 2017
Later among the works it cites.
Compressing neural networks using the variational information bottleneck
Bin Dai, Chen Zhu, Baining Guo, and David Wipf · 2018
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Structured variational learning of bayesian neural networks with horseshoe priors
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Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
Strong NP-hardness for sparse optimization with concave penalty functions
Yichen Chen, Dongdong Ge, Mengdi Wang, Zizhuo Wang, Yinyu Ye, and Hao Yin · 2017
Cited alongside, same era.
Concrete dropout
Yarin Gal, Jiri Hron, and Alex Kendall · 2017
Cited alongside, same era.
From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur
Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, and Qinfeng Shi · 2017
Cited alongside, same era.
Variational gaussian dropout is not bayesian
Jiri Hron, Alexander G de G Matthews, and Zoubin Ghahramani · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Soumya Ghosh, Jiayu Yao, and Finale Doshi-Velez · 2018
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Variational bayesian dropout: pitfalls and fixes
Jiri Hron, Alexander GG Matthews, and Zoubin Ghahramani · 2018
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Deblurring natural image using super-gaussian fields
Yuhang Liu, Wenyong Dong, Dong Gong, Lei Zhang, and Qinfeng Shi · 2018
Closest in time.
Frame-based variational bayesian learning for independent or dependent source separation
Yuhang Liu, Wenyong Dong, and Mengchu Zhou · 2018
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Adversarial dropout for supervised and semi-supervised learning
Sungrae Park, JunKeon Park, Su-Jin Shin, and Il-Chul Moon · 2018
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Overpruning in variational bayesian neural networks
Brian Trippe and Richard Turner · 2018
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Cluster sparsity field: An internal hyperspectral imagery prior for reconstruction
Lei Zhang, Wei Wei, Yanning Zhang, Chunhua Shen, Anton van den Hengel, and Qinfeng Shi · 2018
Closest in time.