AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is shown that neural network weights can be explicilty fintuned to admit a smaller grammar, and the first time grammar size has been used as an explicit training objective for network weights.
We show that a quantized model that keeps its classification accuracy still changes $14$ to $46\%$ of its top-1 retrieval results, and that aggregate ranking metrics reveal only part of this damage. We tie this failure to the gap between the two highest model scores and use that gap to decide when to trust a quantized...
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