331 citations · 655 across the 11 of their papers we have counts for
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stat.ML2017★ 131 cited
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha +1
Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently…
stat.ML2017★ 331 cited
Variational Dropout Sparsifies Deep Neural Networks
Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dr…