Deep Submodular Peripteral Network

arXiv:2403.08199v1 Announce Type: new
Abstract: Submodular functions, crucial for various applications, often lack practical learning methods for their acquisition. Seemingly unrelated, learning a scaling from oracles offering graded pairwise preferences (GPC) is underexplored, despite a rich history in psychometrics. In this paper, we introduce deep submodular peripteral networks (DSPNs), a novel parametric family of submodular functions, and methods for their training using a contrastive-learning inspired GPC-ready strategy to connect and then tackle both of the above challenges. We introduce newly …

abstract acquisition applications arxiv cs.ai cs.lg family functions history network networks novel paper parametric practical psychometrics scaling type