TensorLog: Deep Learning Meets Probabilistic DBs
By William W. Cohen, Fan Yang, Kathryn Mazaitis. 2017
46 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- A.S. d'Avila Garcez
- Luciano Serafini
- L. C. Lamb
- Michael Spranger
- Son Dinh Tran
- Marco Gori
- Pat Verga
- Haitian Sun
Citations in the corpus
- Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, methodology): “Recent research in first-order logic programs has successfully exploited the advantages of distributed representations of logic sym-9 bols for efficient reasoning [6], inductive programming [14, 57, 13], and differentiable theorem proving [39].”
- Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge (2020, background): “…many cognitive architectures which used hybrid symbolic and subsymbolic systems (Laird et al., 2017), and more recently, compositional query languages for embedding KBs that are similar to symbolic KB query languages (Cohen et al., 2017; Hamilton et al., 2018; Ren et al., 2020; Cohen et al., 2020).”
- Neural Logic Reinforcement Learning (2019, methodology): “To address this challenge, recently Differentiable Inductive Logic Programming (DILP) has been proposed in which a learning model expressed by logic states can be trained by gradient-based optimization methods (Evans & Grefenstette, 2018; Rockt¨aschel & Riedel, 2017; Cohen et al., 2017).”