AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Any continuous measure-to-measure operator with outputs of finite $p-th moment can be uniformly approximated, in the $p-Wasserstein distance, by continuous measure-dependent pushforwards, using measure-theoretic transformers.
Long contexts are central to modern transformer systems, but most expressivity results choose a different network for each fixed sequence length. We study whether one masked transformer can approximate causal token-to-token maps uniformly over sequences of arbitrary length sampling a fixed normalized horizon. To relate...
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