Department of Data Science & AI, Monash University, Australia We organize our supplementary material as follows. In Section A, we describe the architecture specifications of LITv2. In Section B, we provide the derivation for the computational cost of HiLo attention. In Section C, we study the effect of window size based on CIFAR-100. In Section F, we provide more visualisation examples for spectrum analysis of HiLo attention. We use "ConvFFN Block" to differentiate our "ConvFFN" denotes our modified FFN layer where we adopt one layer of The overall framework of LITv2 is depicted in Figure I.

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process.

We demonstrate that BootDQNprior+'s lagged target parameters, which are essential to its performance, arise from applying approximate inference to the BBO posterior.