MPNNs, graph transformers do not rely on fixed graph structure information.

There have been approaches to learn such hierarchical structure in sequences such as the HMRNN (Chung et al., 2016). However, as a deterministic model, it has the main limitation that it cannot capture the stochastic nature prevailing in the data. In particular,this is acritical limitation to imagination-augmented agents because exploring various possible futures according to the uncertainty is what makes the imagination meaningful in many cases.

Moreover, we develop stochastic gradient MCMC inference that is scalable to very long multivariate count time series.

Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-totree, tree-to-sequence, and tree-to-tree mappings. Our approach abstracts the transformer'ssinusoidal positional encodings, allowing ustoinstead useanovel positional encoding scheme to represent node positions within trees.

Many real datasets are hierarchically structured.

The high-level question in this work is: If we learn a task using a sufficiently deep NN, how can we uncover the underlying hierarchical organization of sub-functions in that task?