Towards Grounding Conceptual Spaces in Neural Representations
  The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. It aims at bridging the gap between symbolic and
subsymbolic processing. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. In this paper, we present our approach towards grounding the dimensions
of a conceptual space in latent spaces learned by an InfoGAN from unlabeled
data.
