20 citations · 28 across the 12 of their papers we have counts for
7 papers · 1 filter
Efficient Conformal Block Evaluation with GoBlocks
James Chryssanthacopoulos, Vasilis Niarchos, Constantinos Papageorgakis +1
Conformal blocks in odd spacetime dimensions are not known in closed analytic form. To facilitate efficient computations in the conformal bootstrap, we introduce $\texttt{GoBlocks}…
Towards Worst-Case Guarantees with Scale-Aware Interpretability
Lauren Greenspan, David Berman, Aryeh Brill +9
Neural networks organize information according to the hierarchical, multi-scale structure of natural data. Methods to interpret model internals should be similarly scale-aware, exp…
AInstein: Numerical Einstein Metrics via Machine Learning
Edward Hirst, Tancredi Schettini Gherardini, Alexander G. Stapleton
A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry as…
Bayesian RG Flow in Neural Network Field Theories
Jessica N. Howard, Marc S. Klinger, Anindita Maiti +1
The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormal…
NCoder -- A Quantum Field Theory approach to encoding data
David S. Berman, Marc S. Klinger, Alexander G. Stapleton
In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder. The NCoder is a modified autoencoder neural network w…
Bootstrability in Line-Defect CFT with Improved Truncation Methods
V. Niarchos, C. Papageorgakis, P. Richmond +2
We study the conformal bootstrap of 1D CFTs on the straight Maldacena-Wilson line in 4D super-Yang-Mills theory. We introduce an improved truncation scheme with an 'OP…
Bayesian Renormalization
David S. Berman, Marc S. Klinger, Alexander G. Stapleton
In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian Renormalization. The mai…