AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results provide a way to assess the trade-off between stability and representational flexibility directly from block primitives, before training, and improve out-of-distribution generalization in operator learning and accuracy in time-series forecasting.
We propose \emph{the sublinear-growth principle} for deep residual architectures -- a sharp stability threshold on the input-magnitude exponent of every residual block's velocity field: $$\|v(x, t)\| \leq c\,\|x\|^q + b, \qquad q \in [0, 1].$$ The threshold $q = 1$ is established via two independent arguments. Classica...
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