Events

Date: 7 November 2025   Time: 14:30 - 15:30

Location: Room 516, G.O. Jones Building

One important organizing principle for building such neural networks is that of symmetry, i.e. the idea that the symmetries of the problem should be encoded in the architecture of the network. I will provide an introduction to the resulting field of "geometric deep learning". I will then discuss our construction of a neural network that transforms nicely under the conformal group, i.e. the set of transformations that preserve angles. I will describe how our construction leverages some of the kinematics of the AdS/CFT correspondence of quantum gravity, with potential applications to problems in computer vision and critical phenomena. Based on 2505.12880 with Maksim Zhdanov, Erik Bekkers, and Patrick Forré.