3.1k citations · 5.1k across the 43 of their papers we have counts for
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Combining Representation Learning with Logic for Language Processing
Tim Rocktäschel
The current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed ve…
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…
MuFuRU: The Multi-Function Recurrent Unit
Dirk Weissenborn, Tim Rocktäschel
Recurrent neural networks such as the GRU and LSTM found wide adoption in natural language processing and achieve state-of-the-art results for many tasks. These models are characte…
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…