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In this paper we present a proof-of-concept implementation of Neural Theorem Provers (NTPs), end-to-end differentiable counterparts of discrete theorem provers that perform first-order inference on vector representations of symbols using function-free, possibly parameterized, rules. As such, NTPs follow a long tradition of neural-symbolic approaches to automated knowledge base inference, but differ in that they are differentiable with respect to representations of symbols in a knowledge basedoi:10.18653/v1/w16-1309 dblp:conf/akbc/RocktaschelR16 fatcat:5d42tylmcve3rocsjw7d3jueq4