  The Perceiver makes few architectural assumptions about the relationship
among its inputs with quadratic scalability on its memory and computation time.
Indeed, the Perceiver model outpaces or is competitive with ResNet-50 and ViT
in terms of accuracy to some degree. However, the Perceiver does not take
predictive uncertainty and calibration into account. The Perceiver also
generalizes its performance on three datasets, three models, one evaluation
metric, and one hyper-parameter setting. Worst of all, the Perceiver's relative
performance improvement against other models is marginal. Furthermore, its
reduction of architectural prior is not substantial; is not equivalent to its
quality. Thereby, I invented five mutations of the Perceiver, the
Uncertainty-Aware Perceivers, that obtain uncertainty estimates and measured
their performance on three metrics. Experimented with CIFAR-10 and CIFAR-100,
the Uncertainty-Aware Perceivers make considerable performance enhancement
compared to the Perceiver.
