AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
2 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
We derive a tight generalization bound for deterministic neural We derive a tight generalization bound for deterministic neural networks based on their last hidden layer representations. By evaluating representation quality with a parameter-free $k$-nearest-neighbor ($k$-NN) classifier, our certificate avoids tradition...
The natural unit of analysis for these networks may not be through their continuous weight space but instead through their discrete sign codes, which generalize well when same-class inputs share similar sign codes.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.