18 citations · 23 across the 3 of their papers we have counts for
3 papers
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…
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…