533 citations · 1.7k across the 63 of their papers we have counts for
6 papers · 1 filter
Fermions and Supersymmetry in Neural Network Field Theories
Samuel Frank, James Halverson, Anindita Maiti +1
We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann…
Improving Generative Inverse Design of Rectangular Patch Antennas with Test Time Optimization
Beck LaBash, Shahriar Khushrushahi, Fabian Ruehle
We propose a two-stage deep learning framework for the inverse design of rectangular patch antennas. Our approach leverages generative modeling to learn a latent representation of…
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…
Learning Topological Invariance
James Halverson, Fabian Ruehle
Two geometric spaces are in the same topological class if they are related by certain geometric deformations. We propose machine learning methods that automate learning of topologi…
On the Learnability of Knot Invariants: Representation, Predictability, and Neural Similarity
Audrey Lindsay, Fabian Ruehle
We analyze different aspects of neural network predictions of knot invariants. First, we investigate the impact of different knot representations on the prediction of invariants an…
Interpretable Machine Learning for Kronecker Coefficients
Giorgi Butbaia, Kyu-Hwan Lee, Fabian Ruehle
We analyze the saliency of neural networks and employ interpretable machine learning models to predict whether the Kronecker coefficients of the symmetric group are zero or not. Ou…