no code implementations • 6 Nov 2023 • Sean Tull, Razin A. Shaikh, Sara Sabrina Zemljic, Stephen Clark
We show how concepts from the domains of shape, colour, size and position can be learned from images of simple shapes, where concepts are represented as Gaussians in the classical implementation, and quantum effects in the quantum one.
no code implementations • 7 Feb 2023 • Sean Tull, Razin A. Shaikh, Sara Sabrina Zemljic, Stephen Clark
Our approach builds upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains.
no code implementations • 21 Mar 2022 • Razin A. Shaikh, Sara Sabrina Zemljic, Sean Tull, Stephen Clark
In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data.
