Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent

Imen Ayadi, Gabriel Turinici

Open source

DOI: 10.1109/icpr48806.2021.9412831
Published: 2021-01-10
Container: 2020 25th International Conference on Pattern Recognition (ICPR)
Publisher: IEEE
Open access: unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

supportingDOI registered: A matching record was returned by Crossref. Source: Crossref; license: CC0 metadata
supportingDOI resolves: A matching record was returned by Crossref. Source: Crossref; license: CC0 metadata
not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
supportingMetadata completeness: All 6 scored descriptive metadata groups are present. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1109/icpr48806.2021.9412831,
  title = {Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent},
  author = {Imen Ayadi and Gabriel Turinici},
  year = {2021},
  journal = {2020 25th International Conference on Pattern Recognition (ICPR)},
  doi = {10.1109/icpr48806.2021.9412831},
  url = {https://doi.org/10.1109/icpr48806.2021.9412831}
}

RIS

TY  - JOUR
TI  - Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent
AU  - Imen Ayadi
AU  - Gabriel Turinici
PY  - 2021
JO  - 2020 25th International Conference on Pattern Recognition (ICPR)
DO  - 10.1109/icpr48806.2021.9412831
UR  - https://doi.org/10.1109/icpr48806.2021.9412831
ER  -

APA

Ayadi, I., & Turinici, G. (2021). Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent. 2020 25th International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/icpr48806.2021.9412831

Source records

crossref · retrieved 2026-09-25T20:49:46.213Z