Implicit Hypersurface Approximation Capacity in Deep ReLU Networks
<|reference_start|>Implicit Hypersurface Approximation Capacity in Deep ReLU Networks: We develop a geometric approximation theory for deep feed-forward neural networks with ReLU activations. Given a $d$-dimensional hypersurface in $\mathbb{R}^{d+1}$ represented as the graph of a $C^2$-function $\phi$, we show that a deep fully-connected ReLU network of width $d+1$ can implicitly construct an approximation as its zero contour with a precision bound depending on the number of layers. This result is directly applicable to the binary classification setting where the sign of the network is trained as a classifier, with the network's zero contour as a decision boundary. Our proof is constructive and relies on the geometrical structure of ReLU layers provided in [doi:10.48550/arXiv.2310.03482]. Inspired by this geometrical description, we define a new equivalent network architecture that is easier to interpret geometrically, where the action of each hidden layer is a projection onto a polyhedral cone derived from the layer's parameters. By repeatedly adding such layers, with parameters chosen such that we project small parts of the graph of $\phi$ from the outside in, we, in a controlled way, construct a network that implicitly approximates the graph over a ball of radius $R$. The accuracy of this construction is controlled by a discretization parameter $\delta$ and we show that the tolerance in the resulting error bound scales as $(d-1)R^{3/2}\delta^{1/2}$ and the required number of layers is of order $d\big(\frac{32R}{\delta}\big)^{\frac{d+1}{2}}$.<|reference_end|>
@article{vallin2024implicit,
    title={Implicit Hypersurface Approximation Capacity in Deep ReLU Networks},
    author={Jonatan Vallin, Karl Larsson, Mats G. Larson},
    journal={arXiv preprint arXiv:2407.03851},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03851},
    primaryClass={cs.LG}
}