  The goal of combining the robustness of neural networks and the expressivity
of symbolic methods has rekindled the interest in neuro-symbolic AI. Recent
advancements in neuro-symbolic AI often consider specifically-tailored
architectures consisting of disjoint neural and symbolic components, and thus
do not exhibit desired gains that can be achieved by integrating them into a
unifying framework. We introduce SLASH -- a novel deep probabilistic
programming language (DPPL). At its core, SLASH consists of
Neural-Probabilistic Predicates (NPPs) and logical programs which are united
via answer set programming. The probability estimates resulting from NPPs act
as the binding element between the logical program and raw input data, thereby
allowing SLASH to answer task-dependent logical queries. This allows SLASH to
elegantly integrate the symbolic and neural components in a unified framework.
We evaluate SLASH on the benchmark data of MNIST addition as well as novel
tasks for DPPLs such as missing data prediction and set prediction with
state-of-the-art performance, thereby showing the effectiveness and generality
of our method.
