By combining a caching algorithm, namely Greedy Dual Size with Frequency (GDSF) or Least Expected Cost (LEC), with a model multiplexer, we achieve optimal rates in both offline and online settings.

However, existing MP-NAS methods face architectural limitations. These limitations hinder MP-NAS usage in SOT A search spaces, leaving the challenge of swiftly designing effective large models unresolved. Accuracy is the result of the network training on ImageNet for 200 epochs. An accuracy prediction model that operates without FLOPs information. Table 2 illustrates the outcomes of these models.

However, designing large models by NAS is challenging due to the dramatical increase of search space and the associated huge performance evaluation cost.

In this paper, we introduce alowrank interaction and sparse additive effects(LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects andinteractions simultaneously.

DiTs (U-DiTs) in the paper and conduct extensive experiments to demonstrate the extraordinary performance of U-DiT models.