◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

L. Markeeva

4 papers here

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.NE1

identity via Semantic Scholar / OpenAlex

activity
20192021
most citedTowards Understanding Normalization in Neural ODEs

5 citations · 9 across the 2 of their papers we have counts for

collaborators

4 papers

cs.LG2021★ 4 cited

A Generalized Lottery Ticket Hypothesis

Ibrahim Alabdulmohsin, Larisa Markeeva, Daniel Keysers +1

We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters. We present ev…

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…

cs.LG2019

MUSCO: Multi-Stage Compression of neural networks

Julia Gusak, Maksym Kholiavchenko, Evgeny Ponomarev +3

The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.