61 citations · 95 across the 8 of their papers we have counts for
8 papers
Symmetry-preserving neural networks in lattice field theories
Matteo Favoni
This thesis deals with neural networks that respect symmetries and presents the advantages in applying them to lattice field theory problems. The concept of equivariance is explain…
Applications of Lattice Gauge Equivariant Neural Networks
Matteo Favoni, Andreas Ipp, David I. Müller
The introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving their performance. In lattice gau…
Equivariance and generalization in neural networks
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
The crucial role played by the underlying symmetries of high energy physics and lattice field theories calls for the implementation of such symmetries in the neural network archite…
Generalization capabilities of neural networks in lattice applications
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories. An essential element of such theories is represented by symme…
Preserving gauge invariance in neural networks
Matteo Favoni, Andreas Ipp, David I. Müller +1
In these proceedings we present lattice gauge equivariant convolutional neural networks (L-CNNs) which are able to process data from lattice gauge theory simulations while exactly…
Lattice gauge symmetry in neural networks
Matteo Favoni, Andreas Ipp, David I. Müller +1
We review a novel neural network architecture called lattice gauge equivariant convolutional neural networks (L-CNNs), which can be applied to generic machine learning problems in…
Generalization capabilities of translationally equivariant neural networks
Srinath Bulusu, Matteo Favoni, Andreas Ipp +2
The rising adoption of machine learning in high energy physics and lattice field theory necessitates the re-evaluation of common methods that are widely used in computer vision, wh…
Lattice gauge equivariant convolutional neural networks
Matteo Favoni, Andreas Ipp, David I. Müller +1
We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems. At the heart of this ne…