102 citations · 277 across the 29 of their papers we have counts for
15 papers · 1 filter
Bayesian Sparsification of Gated Recurrent Neural Networks
Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov
Bayesian methods have been successfully applied to sparsify weights of neural networks and to remove structure units from the networks, e. g. neurons. We apply and further develop…
Variational Dropout via Empirical Bayes
Valery Kharitonov, Dmitry Molchanov, Dmitry Vetrov
We study the Automatic Relevance Determination procedure applied to deep neural networks. We show that ARD applied to Bayesian DNNs with Gaussian approximate posterior distribution…
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks
Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov
Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, r…
Pairwise Augmented GANs with Adversarial Reconstruction Loss
Aibek Alanov, Max Kochurov, Daniil Yashkov +1
We propose a novel autoencoding model called Pairwise Augmented GANs. We train a generator and an encoder jointly and in an adversarial manner. The generator network learns to samp…
Bayesian Compression for Natural Language Processing
Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov
In natural language processing, a lot of the tasks are successfully solved with recurrent neural networks, but such models have a huge number of parameters. The majority of these p…
Metropolis-Hastings view on variational inference and adversarial training
Kirill Neklyudov, Evgenii Egorov, Pavel Shvechikov +1
A significant part of MCMC methods can be considered as the Metropolis-Hastings (MH) algorithm with different proposal distributions. From this point of view, the problem of constr…