AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric conditioning, the convergence rate of first-order iterative algorithms, and the stability of regression estimators to target perturbations. Despite their importance, explicit...
This work proposes a computational tropical geometry framework for the symbolic analysis of neural networks with tropical activations, and presents an algorithm, together with a proof of correctness, which computes the linear regions of a neural network as explicit unions of polyhedra.
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