533 citations · 558 across the 14 of their papers we have counts for
5 papers · 1 filter
Conformal Fields from Neural Networks
James Halverson, Joydeep Naskar, Jiahua Tian
We use the embedding formalism to construct conformal fields in dimensions, by restricting Lorentz-invariant ensembles of homogeneous neural networks in dimensions to t…
On the Generality and Persistence of Cosmological Stasis
James Halverson, Sneh Pandya
Hierarchical decays of matter species to radiation may balance against Hubble expansion to yield stasis, a new phase of cosmological evolution with constant matter and radiatio…
TASI Lectures on Physics for Machine Learning
Jim Halverson
These notes are based on lectures I gave at TASI 2024 on Physics for Machine Learning. The focus is on neural network theory, organized according to network expressivity, statistic…
KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya +5
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have…
Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
Sergei Gukov, James Halverson, Fabian Ruehle
Machine learning techniques are increasingly powerful, leading to many breakthroughs in the natural sciences, but they are often stochastic, error-prone, and blackbox. How, then, s…