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Learning Reasoning Strategies in End-to-End Differentiable Proving
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp +2
Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theor…
Logical Rule Induction and Theory Learning Using Neural Theorem Proving
Andres Campero, Aldo Pareja, Tim Klinger +2
A hallmark of human cognition is the ability to continually acquire and distill observations of the world into meaningful, predictive theories. In this paper we present a new mecha…
Towards Neural Theorem Proving at Scale
Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel +1
Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their c…
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…
Knowledge Graph Completion via Complex Tensor Factorization
Théo Trouillon, Christopher R. Dance, Johannes Welbl +3
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predic…
Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel +2
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to sol…