Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis.
- DOI
- 10.3389/fncom.2026.1791546
- Published
- 2026
- Container
- Frontiers in computational neuroscience
- Publisher
- Not recorded
- Open access
- yes
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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 = {Sakabe EY and Abrahão FS and Simões A and Colombini E and Costa P and Gudwin R and Zenil H},
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 - Sakabe EY AU - Abrahão FS AU - Simões A AU - Colombini E AU - Costa P AU - Gudwin R AU - Zenil H PY - 2026 JO - Frontiers in computational neuroscience DO - 10.3389/fncom.2026.1791546 UR - https://doi.org/10.3389/fncom.2026.1791546 ER -
APA
EY, S., FS, A., A, S., E, C., P, C., R, G., & H, Z. (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
- pubmed · retrieved 2026-09-25T07:02:07.811Z