AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The flatness of the loss landscape at a minimizer is a widely used heuristic for reasoning about neural-network generalization, yet evidence for this relation is mostly empirical and controversial, and it is found that the answer strongly depends on the learning task and on the ratio of the number of parameters to the...
Using a finite energy message-passing algorithm, it is demonstrated numerically that thermal noise enables effective generalization in the regime of constraint densities where both recovering the teacher and finding a zero temperature solution are computationally hard.
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