We use a neural network which consists of a weight matrix W 1 ∈ R n×n for left children and a weight matrix W 2 ∈ R n×n for right children to compute the vector for a parent node in a bottom up manner.
we compute p 1 as follows where b is a bias vector and f is an activation function (e.g., tanh or logistic).