TensorLog: Deep Learning Meets Probabilistic DBs
arXiv: Artificial Intelligence(2017)
摘要
We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neural-network infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning frameworks to be used for tuning the parameters of a probabilistic logic. Experimental results show that TensorLog scales to problems involving hundreds of thousands of knowledge-base triples and tens of thousands of examples.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络