Hypercube Neural Topologies: Enhancing Depth Efficiency and Gradient Flow in Deep Networks.
- DOI
- 10.1109/tnnls.2026.3699539
- Published
- 2026 Nov
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2026.3699539,
title = {Hypercube Neural Topologies: Enhancing Depth Efficiency and Gradient Flow in Deep Networks.},
author = {Park BJ and Han DS},
year = {2026},
journal = {IEEE transactions on neural networks and learning systems},
doi = {10.1109/tnnls.2026.3699539},
url = {https://doi.org/10.1109/tnnls.2026.3699539}
}RIS
TY - JOUR TI - Hypercube Neural Topologies: Enhancing Depth Efficiency and Gradient Flow in Deep Networks. AU - Park BJ AU - Han DS PY - 2026 JO - IEEE transactions on neural networks and learning systems DO - 10.1109/tnnls.2026.3699539 UR - https://doi.org/10.1109/tnnls.2026.3699539 ER -
APA
BJ, P., & DS, H. (2026). Hypercube Neural Topologies: Enhancing Depth Efficiency and Gradient Flow in Deep Networks.. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2026.3699539
Source records
- pubmed · retrieved 2026-11-10T11:58:46.847Z