Paper detail

Tree Echo State Autoencoders with Grammars

Tree data occurs in many forms, such as computer programs, chemical molecules, or natural language. Unfortunately, the non-vectorial and discrete nature of trees makes it challenging to construct functions with tree-formed output, complicating tasks such as optimization or time series prediction. Autoencoders address this challenge by mapping trees to a vectorial latent space, where tasks are easier to solve, and then mapping the solution back to a tree structure. However, existing autoencoding approaches for tree data fail to take the specific grammatical structure of tree domains into account and rely on deep learning, thus requiring large training datasets and long training times. In this paper, we propose tree echo state autoencoders (TES-AE), which are guided by a tree grammar and can be trained within seconds by virtue of reservoir computing. In our evaluation on three datasets, we demonstrate that our proposed approach is not only much faster than a state-of-the-art deep learning autoencoding approach (D-VAE) but also has less autoencoding error if little data and time is given.