13 citations · 33 across the 8 of their papers we have counts for
5 papers · 1 filter
On the Embeddings of Variables in Recurrent Neural Networks for Source Code
Nadezhda Chirkova
Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher q…
A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code
Nadezhda Chirkova, Sergey Troshin
There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to softw…
Empirical Study of Transformers for Source Code
Nadezhda Chirkova, Sergey Troshin
Initially developed for natural language processing (NLP), Transformers are now widely used for source code processing, due to the format similarity between source code and text. I…
On Power Laws in Deep Ensembles
Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan +1
Ensembles of deep neural networks are known to achieve state-of-the-art performance in uncertainty estimation and lead to accuracy improvement. In this work, we focus on a classifi…
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…