The six pooling functions, where x_i refers to the embedding vector of instance <i>i</i> in a bag set <i>B</i> and <i>k</i> is a particular element of the output vector h.

In the multi-attention equation, L refers to the attended layer and w is a learned weight. The attention module outputs are concatenated before being passed to the output layer. In the feature-level attention equation, q(⋅) is an attention function on a representation of the input features, u(⋅).