16 citations · 78 across the 28 of their papers we have counts for
10 papers · 1 filter
Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against thei…
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from…
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond +1
The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' crite…
Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model
Eyup B. Unlu, Roy T. Forestano, Konstantin T. Matchev +1
We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of…
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…
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +1
The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data…
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…