141 papers with code • 1 benchmarks • 12 datasets
The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.