VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming

Eleonora Misino, Giuseppe Marra, Emanuele Sansone

Abstract:

We present VAEL, a neuro-symbolic generative model integrating variational autoencoders
(VAE) with the reasoning capabilities of probabilistic logic (L) programming. Besides standard
latent subsymbolic variables, our model exploits a probabilistic logic program to define a further
structured representation, which is used for logical reasoning. The entire process is end-to-end
differentiable. Once trained, VAEL can solve new unseen generation tasks by (i) leveraging the
previously acquired knowledge encoded in the neural component and (ii) exploiting new logical
programs on the structured latent space. Our experiments provide support on the benefits of
this neuro-symbolic integration both in terms of task generalization and data efficiency. To the
best of our knowledge, this work is the first to propose a general-purpose end-to-end framework
integrating probabilistic logic programming into a deep generative model.