9 citations · 28 across the 17 of their papers we have counts for
5 papers · 1 filter
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone t…
Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry ge…
Discovering Sparse Representations of Lie Groups with Machine Learning
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter,…
Oracle-Preserving Latent Flows
Alexander Roman, Roy T. Forestano, Konstantin T. Matchev +2
We develop a deep learning methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset. The symmetry transformations a…
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks…