AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper proposes an algorithm that leverages information about the distribution of the data to find better cuts, and extends the algorithm and the theoretical guarantees it provides to kernel clustering, thereby refining the existing worst-case analysis.
It is shown that pooling augmentations together, despite inter-dependencies, is a better alternative than the baseline of partitioning the data into subsets of independent data and provides an intuition behind the practical preference towards using many augmentations.
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