Talks

Large neural networks are often studied analytically through scaling limits: regimes in which some structural network parameters (e.g. depth, width, number of training datapoints, and so on) tend to infinity. Such limits are challenging to identify and study in part because the limits as these structural parameters diverge typically do not commute. I will present some recent and ongoing work with Alexander Zlokapa (MIT), in which we provide the first solvable models of learning – in this case by Bayesian inference – with neural networks where the depth, width, and number of datapoints can all be large.

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A formulation of the 2-dimensional Ising model on a triangulated Riemann sphere is proposed that converges to the exact conformal field theory (CFT) in the continuum limit. The solution is based on reconciling Regge's simplicial geometry for the Einstein Hilbert action with an Affine map to quantum correlators on the tangent plane. Numerical tests of the 2d Ising sphere and radial quantized phi4 theory on $R x S^2$ are presented.  Extending the method to more general fields theories on curved manifolds is discussed. 

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At the classical level, one can restrict a spherically symmetric model to the corresponding LTB sector by requiring that a so-called LTB condition is satisfied. In this talk we will discuss how this can be generalised in the context of effective models containing quantum gravity inspired modifications of  the classical theory in the form of polymerisations. The formalism presented allows us to consider more general polymerisations than in previous work in the literature. Applications of this framework are then considered, focusing on a particular class of models that have the property that the effective dynamics is completely decoupled along the radial direction. Examples of effective models that fall into this class are discussed and their physical properties are compared with existing models in the literature.

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