  Deep learning techniques have been hugely successful for traditional
supervised and unsupervised machine learning problems. In large part, these
techniques solve continuous optimization problems. Recently however, discrete
generative deep learning models have been successfully used to efficiently
search high-dimensional discrete spaces. These methods work by representing
discrete objects as sequences, for which powerful sequence-based deep models
can be employed. Unfortunately, these techniques are significantly hindered by
the fact that these generative models often produce invalid sequences. As a
step towards solving this problem, we propose to learn a deep recurrent
validator model. Given a partial sequence, our model learns the probability of
that sequence occurring as the beginning of a full valid sequence. Thus this
identifies valid versus invalid sequences and crucially it also provides
insight about how individual sequence elements influence the validity of
discrete objects. To learn this model we propose an approach inspired by
seminal work in Bayesian active learning. On a synthetic dataset, we
demonstrate the ability of our model to distinguish valid and invalid
sequences. We believe this is a key step toward learning generative models that
faithfully produce valid discrete objects.
