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Found 12 papers, 4 papers with code
KAN: Kolmogorov-Arnold Networks
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs).
Rigor with Machine Learning from Field Theory to the Poincaré Conjecture
Machine learning techniques are increasingly powerful, leading to many breakthroughs in the natural sciences, but they are often stochastic, error-prone, and blackbox.
Metric Flows with Neural Networks
We develop a theory of flows in the space of Riemannian metrics induced by neural network gradient descent.
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Conversely, the correspondence allows one to engineer architectures realizing a given field theory by representing action deformations as deformations of neural network parameter densities.
Searching for ribbons with machine learning
We apply Bayesian optimization and reinforcement learning to a problem in topology: the question of when a knot bounds a ribbon disk.
Building Quantum Field Theories Out of Neurons
An approach to field theory is studied in which fields are comprised of $N$ constituent random neurons.
Infinite Neural Network Quantum States: Entanglement and Training Dynamics
We study infinite limits of neural network quantum states ($\infty$-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics.
Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators
We demonstrate that symmetries of network densities may be determined via dual computations of network correlation functions, even when the density is unknown and the network is not equivariant.
Learning to Unknot
We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots.
Neural Networks and Quantum Field Theory
We propose a theoretical understanding of neural networks in terms of Wilsonian effective field theory.
Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
In one case, we demonstrate that the agent learns a human-derived strategy for finding consistent string models.
High Energy Physics - Theory
Dark Glueballs and their Ultralight Axions
Dark gauge sectors and axions are well-motivated in string theory.
High Energy Physics - Phenomenology High Energy Physics - Theory
