Neural Networks as Inter-Domain Inducing Points
Equivalences between infinite neural networks and Gaussian processes have been established for explaining the functional prior and training dynamics of deep learning models. In this paper we cast the hidden units of finite-width neural networks as the inter-domain inducing points of a kernel, then a one-hidden-layer network becomes a kernel regression model. For dot-product kernels on both $R^d$ and $S^{d−1}$, we derive the kernel functions for inducing points. Empirically we conduct toy experiments to validate the proposed approaches.
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