Title: Grammar Variational Autoencoder,
Abstract: Deep generative models have been wildly successful at learning coherent
latent representations for continuous data such as video and audio. However,
generative modeling of discrete data such as arithmetic expressions and
molecular structures still poses significant challenges. Crucially,
state-of-the-art methods often produce outputs that are not valid. We make the
key observation that frequently, discrete data can be represented as a parse
tree from a context-free grammar. We propose a variational autoencoder which
encodes and decodes directly to and from these parse trees, ensuring the
generated outputs are always valid. Surprisingly, we show that not only does
our model more often generate valid outputs, it also learns a more coherent
latent space in which nearby points decode to similar discrete outputs. We
demonstrate the effectiveness of our learned models by showing their improved
performance in Bayesian optimization for symbolic regression and molecular
synthesis.