604 citations · 2.5k across the 63 of their papers we have counts for
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Probabilistic Adaptive Computation Time
Michael Figurnov, Artem Sobolev, Dmitry Vetrov
We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables…
Bayesian Sparsification of Recurrent Neural Networks
Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov
Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights. Recently proposed Sparse Variational D…
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…
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…