Arch-Net: Model conversion and quantization for architecture agnostic model deployment.
- DOI
- 10.1016/j.neunet.2025.107384
- Published
- 2025 Jul
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2025.107384,
title = {Arch-Net: Model conversion and quantization for architecture agnostic model deployment.},
author = {Fang S and Xu W and Feng Z and Yuan S and Wang Y and Yang Y and Ding W and Zhou S},
year = {2025},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2025.107384},
url = {https://doi.org/10.1016/j.neunet.2025.107384}
}RIS
TY - JOUR TI - Arch-Net: Model conversion and quantization for architecture agnostic model deployment. AU - Fang S AU - Xu W AU - Feng Z AU - Yuan S AU - Wang Y AU - Yang Y AU - Ding W AU - Zhou S PY - 2025 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2025.107384 UR - https://doi.org/10.1016/j.neunet.2025.107384 ER -
APA
S, F., W, X., Z, F., S, Y., Y, W., Y, Y., W, D., & S, Z. (2025). Arch-Net: Model conversion and quantization for architecture agnostic model deployment.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2025.107384
Source records
- pubmed · retrieved 2026-09-27T00:42:02.823Z