13 citations · 19 across the 3 of their papers we have counts for
4 papers
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov
One of the generally accepted views of modern deep learning is that increasing the number of parameters usually leads to better quality. The two easiest ways to increase the number…
Structured Sparsification of Gated Recurrent Neural Networks
Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich +1
Recently, a lot of techniques were developed to sparsify the weights of neural networks and to remove networks' structure units, e.g. neurons. We adjust the existing sparsification…
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…
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…