In contrast, Eq. (5) represents the kernel as a product of two kernels (one for f i and another for t i ), which is able to capture the similarities for both temporal and non-temporal components. (ii) Transformer-XL<cite> (Dai et al., 2019)</cite> , Music Transformer (Huang et al., 2018b) , Self-Attention with Relative Positional Embedding (Shaw et al., 2018) :