Jan 19, 2017 · Abstract:We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout.
Our experiments show that Sparse Variational Dropout leads to a high level of sparsity in fully-connected and con- volutional layers of Deep Neural Networks.
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The plot contains the accuracy and sparsity level for VGG-like architectures of different sizes. The number of neurons and filters scales as k. Dense networks ...
We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational ...
Variational Dropout is extended to the case when dropout rates are unbounded, a way to reduce the variance of the gradient estimator is proposed and first ...
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The discovered approach helps to train both convolutional and dense deep sparsified models without significant loss of quality.
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Variational dropout sparsifies deep neural networks. D Molchanov, A Ashukha, D Vetrov. International conference on machine learning, 2498-2507, 2017. 1007, 2017.