Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VAEL: Bridging Variational Autoencoders and Probabilistic Logic
  Programming   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.

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The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg