Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis
- DOI
- 10.3389/fncom.2026.1791546
- Published
- 2026-05-29
- Container
- Frontiers in Computational Neuroscience
- Publisher
- Frontiers Media SA
- 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.
- supportingDOI resolves: A matching record was returned by Crossref.
- 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.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- 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.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.3389/fncom.2026.1791546,
title = {Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis},
author = {Eduardo Y. Sakabe and Felipe S. Abrahão and Alexandre Simões and Esther Colombini and Paula Costa and Ricardo Gudwin and Hector Zenil},
year = {2026},
journal = {Frontiers in Computational Neuroscience},
doi = {10.3389/fncom.2026.1791546},
url = {https://doi.org/10.3389/fncom.2026.1791546}
}RIS
TY - JOUR TI - Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis AU - Eduardo Y. Sakabe AU - Felipe S. Abrahão AU - Alexandre Simões AU - Esther Colombini AU - Paula Costa AU - Ricardo Gudwin AU - Hector Zenil PY - 2026 JO - Frontiers in Computational Neuroscience DO - 10.3389/fncom.2026.1791546 UR - https://doi.org/10.3389/fncom.2026.1791546 ER -
APA
Sakabe, E. Y., Abrahão, F. S., Simões, A., Colombini, E., Costa, P., Gudwin, R., & Zenil, H. (2026). Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis. Frontiers in Computational Neuroscience. https://doi.org/10.3389/fncom.2026.1791546
Source records
- crossref · retrieved 2026-11-09T03:39:23.006Z