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A Lightweight and Gradient-Stable Neural Layer
March 27, 2024, 4:43 a.m. | Yueyao Yu, Yin Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: To enhance resource efficiency and model deployability of neural networks, we propose a neural-layer architecture based on Householder weighting and absolute-value activating, called Householder-absolute neural layer or simply Han-layer. Compared to a fully connected layer with $d$-neurons and $d$ outputs, a Han-layer reduces the number of parameters and the corresponding computational complexity from $O(d^2)$ to $O(d)$. {The Han-layer structure guarantees that the Jacobian of the layer function is always orthogonal, thus ensuring gradient stability (i.e., …
abstract architecture arxiv cs.lg efficiency gradient layer networks neural networks neurons parameters resource efficiency type value
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