论文标题
VAEL：桥接变异自动编码器和概率逻辑编程
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
论文作者
论文摘要
我们提出了Vael，这是一种神经符号生成模型，将变异自动编码器（VAE）与概率逻辑（L）编程的推理能力集成在一起。除了标准潜在的子符号变量外，我们的模型还利用了一个概率逻辑程序来定义进一步的结构化表示，该表示用于逻辑推理。整个过程都是端到端的。一旦受过培训，Vael可以通过（i）利用在神经组件中编码的先前获得的知识以及（ii）利用结构性潜在空间上的新逻辑程序来解决新的看不见的生成任务。我们的实验在任务概括和数据效率方面为这种神经符号整合的好处提供了支持。据我们所知，这项工作是第一个提出通用端到端框架的工作，将概率逻辑编程整合到一个深层的生成模型中。
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.
