  \textit{Attention} computes the dependency between representations, and it
encourages the model to focus on the important selective features.
Attention-based models, such as Transformer and graph attention network (GAT),
are widely utilized for sequential data and graph-structured data. This paper
suggests a new interpretation and generalized structure of the attention in
Transformer and GAT. For the attention in Transformer and GAT, we derive that
the attention is a product of two parts: 1) the RBF kernel to measure the
similarity of two instances and 2) the exponential of $L^{2}$ norm to compute
the importance of individual instances. From this decomposition, we generalize
the attention in three ways. First, we propose implicit kernel attention with
an implicit kernel function instead of manual kernel selection. Second, we
generalize $L^{2}$ norm as the $L^{p}$ norm. Third, we extend our attention to
structured multi-head attention. Our generalized attention shows better
performance on classification, translation, and regression tasks.
