3.1k citations · 5.1k across the 43 of their papers we have counts for
5 papers · 1 filter
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…
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
Gregory Farquhar, Tim Rocktäschel, Maximilian Igl +1
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has p…
Adversarial Sets for Regularising Neural Link Predictors
Pasquale Minervini, Thomas Demeester, Tim Rocktäschel +1
In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances. However, in relational learning and other non-i.i.d d…
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…
Frustratingly Short Attention Spans in Neural Language Modeling
Michał Daniluk, Tim Rocktäschel, Johannes Welbl +1
Neural language models predict the next token using a latent representation of the immediate token history. Recently, various methods for augmenting neural language models with an…