80 papers with code • 1 benchmarks • 9 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.
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Ranked #4 on Graph Classification on REDDIT-B
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.
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.
Ranked #2 on Knowledge Graphs on FB15k
In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection.
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models.
We achieve state of the art results on the bAbI textual question-answering dataset with the recurrent relational network, consistently solving 20/20 tasks.
Ranked #3 on Question Answering on bAbi (Mean Error Rate metric)