6 papers
What Neural Network Field Theory Can and Cannot Realise on a Computer
Thomas R. Harvey
One aim of neural network field theory is to put a quantum or effective field theory on a computer, with the network ensemble itself as the theory. We ask how far that aim can be p…
Naturalness and Fisher Information
James Halverson, Thomas R. Harvey, Michael Nee
Fine-tuning and naturalness, the sensitivity of low-energy observables to small changes in the fundamental parameters of a theory, are cornerstones of physics beyond the Standard M…
The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric
Thomas R. Harvey
We present a class of novel optimisers for training neural networks that makes use of the Riemannian metric naturally induced when the loss landscape is embedded in higher-dimensio…
Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks
Thomas R. Harvey, Fabian Ruehle, Kit Fraser-Taliente +1
We present a novel approach to symbolic regression using vision-capable large language models (LLMs) and the ideas behind Google DeepMind's Funsearch. The LLM is given a plot of a…
Generative Modeling for Mathematical Discovery
Jordan S. Ellenberg, Cristofero S. Fraser-Taliente, Thomas R. Harvey +2
We present a new implementation of the LLM-driven genetic algorithm {\it funsearch}, whose aim is to generate examples of interest to mathematicians and which has already had some…
Not So Flat Metrics
Kit Fraser-Taliente, Thomas R. Harvey, Manki Kim
In order to be in control of the derivative expansion, geometric string compactifications are understood in the context of a large volume approximation. In this letter, we co…