Learning Hierarchical Relational Representations through Relational Convolutions

arXiv:2310.03240v2 Announce Type: replace
Abstract: A maturing area of research in deep learning is the study of architectures and inductive biases for learning representations of relational features. In this paper, we focus on the problem of learning representations of hierarchical relations, proposing an architectural framework we call "relational convolutional networks". Given a collection of objects, pairwise relations are modeled via inner products of feature maps. We formalize a relational convolution operation in which graphlet filters are matched against patches of …

abstract architectures arxiv biases call collection cs.lg deep learning features focus framework hierarchical inductive networks paper relational relations research study through type