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Julia Gusak

INRIA

13 papers hereh-index 11463 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author8
  • last author1

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • cs.AI1
  • cs.CV1
  • cs.NE1
affiliations
  • INRIA
Homepage

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedSurvey on Large Scale Neural Network Training

8 citations · 24 across the 8 of their papers we have counts for

collaborators
Showing 2020Show all

3 papers · 1 filter

cs.CV2020★ 2 cited

Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network

Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin +6

Most state of the art deep neural networks are overparameterized and exhibit a high computational cost. A straightforward approach to this problem is to replace convolutional kerne…

cs.LG2020★ 5 cited

Towards Understanding Normalization in Neural ODEs

Julia Gusak, Larisa Markeeva, Talgat Daulbaev +3

Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly…

cs.NE2020

Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs

Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva +3

We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models. We compare it with the reverse dynamic method (known in the litera…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.