3.1k citations · 6.9k across the 91 of their papers we have counts for
3 papers · 1 filter
End-to-End Differentiable Proving
Tim Rocktäschel, Sebastian Riedel
We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are…
Learning Python Code Suggestion with a Sparse Pointer Network
Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr +1
To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While…
Programming with a Differentiable Forth Interpreter
Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky +1
Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider t…