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.
Eleonora Misino (University of Bologna)
Giuseppe Marra (KU Leuven)
Emanuele Sansone (KU Leuven)
More from the Same Authors
2022 Poster: Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off »
Mateo Espinosa Zarlenga · Pietro Barbiero · Gabriele Ciravegna · Giuseppe Marra · Francesco Giannini · Michelangelo Diligenti · Zohreh Shams · Frederic Precioso · Stefano Melacci · Adrian Weller · Pietro Lió · Mateja Jamnik