arXiv Machine Learning By Manas Dogra, James Halverson, Joydeep Naskar

Spinning Conformal Correlators from Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2608. 15001v1 Announce Type: cross Abstract: We construct spinning conformal fields from neural networks and the embedding formalism, computing their two-, three- and four-point functions in examples, building on scalar conformal field techniques introduced in \cite{Halverson:2024axc}.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 11

Aggregation in conformal e-classification

arXiv:2605. 07963v2 Announce Type: replace Abstract: Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least approximately.

By Vladimir Vovk
arXiv Machine Learning
Jun 25

Two-dimensional Hyperbolic RNN Neural Quantum State

arXiv:2606. 25600v1 Announce Type: cross Abstract: In the first part of this work, we construct the first type of two-dimensional (2D) hyperbolic neural quantum state (NQS) in the form of the Lorentz 2DRNN (Recurrent Neural Network) and benchmark its performance against the Euclidean 2DRNN in the paradigmatic $N\times N$ 2D Transverse Field Ising Model (2DTFIM) setting with different lattice sizes up to $N=12$ and at different transverse magnetic field strengths.

By H. L. Dao
arXiv Machine Learning
Aug 4

Descending into the Modular Bootstrap

arXiv:2604. 01275v3 Announce Type: replace-cross Abstract: In this paper, we attempt to explore the landscape of two-dimensional conformal field theories (2d CFTs) by efficiently searching for numerical solutions to the modular bootstrap equation using machine-learning-style optimization.

By Nathan Benjamin, A. Liam Fitzpatrick, Wei Li, Jesse Thaler