AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results show that learning under an appropriate prior overcomes the curse of dimensionality with respect to the dependence on $n$ and introduces the class of sparse priors and defines the "Sparse Dimension" as a measure of sparsity of a prior over the space of all distributions.
This work proves the existence of an optimal hyperparameter configuration for which the LGD algorithm achieves the Bayes'optimal solution for squared loss on regression tasks, and shows empirical evidence of the success of LGD and the meta learning procedure for few-shot learning on linear and logistic regression using...
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