Fixed, Learned, or Both? A Machine-Learning Study of Self-Calibrating Gated Couplings of Transform Dictionaries and Learned Unitary Bases for Compact Signal Classification
- DOI
- 10.5281/zenodo.21644586
- Published
- 2026
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- Not recorded
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- Zenodo
- Open access
- yes
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BibTeX
@article{allodium:10.5281/zenodo.21644586,
title = {Fixed, Learned, or Both? A Machine-Learning Study of Self-Calibrating Gated Couplings of Transform Dictionaries and Learned Unitary Bases for Compact Signal Classification},
author = {del Rio Romero, Juan Carlos},
year = {2026},
doi = {10.5281/zenodo.21644586},
url = {https://doi.org/10.5281/zenodo.21644586}
}RIS
TY - JOUR TI - Fixed, Learned, or Both? A Machine-Learning Study of Self-Calibrating Gated Couplings of Transform Dictionaries and Learned Unitary Bases for Compact Signal Classification AU - del Rio Romero, Juan Carlos PY - 2026 DO - 10.5281/zenodo.21644586 UR - https://doi.org/10.5281/zenodo.21644586 ER -
APA
Carlos, D. R. R. J. (2026). Fixed, Learned, or Both? A Machine-Learning Study of Self-Calibrating Gated Couplings of Transform Dictionaries and Learned Unitary Bases for Compact Signal Classification. https://doi.org/10.5281/zenodo.21644586