Here, Linear ϕ denotes a linear layer followed by an activation function ϕ, SAB n (•) represents n subsequent applications of a SAB, and S k is a collection of learnable k seed vectors that are randomly initialised (PMA k outputs k vectors). The resulting Set Transformer module can be used as a pooling function by encoding all the atomic representations into features Z that are transformed into a single-vector representation by the decoder. Here, we refer to this pooling function as attention-based pooling (ABP):