Hypercube Neural Topologies: Enhancing Depth Efficiency and Gradient Flow in Deep Networks
- DOI
- 10.1109/tnnls.2026.3699539
- Published
- 2026
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- 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 = {Byeong-Jun Park and Dong Seog Han},
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 - Byeong-Jun Park AU - Dong Seog Han 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
Park, B., & Han, D. S. (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
- crossref · retrieved 2026-09-25T12:28:19.617Z