AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work builds Generative AdS, an approach to generative flow matching in which the dynamics are represented by the evolution of fields in AdS, together with a residual correction learned by a neural network.
Analytical and particle-based methods for uncertainty propagation in random neural network models, where both the inputs and network parameters are allowed to be random, are developed and extended to autonomous dynamical systems whose one-step evolution map is represented by a random neural network.
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