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Found 15 papers, 2 papers with code
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks.
Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters.
$\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN).
Seeking Truth and Beauty in Flavor Physics with Machine Learning
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' criteria like beauty, naturalness, etc.
Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model
We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex.
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage.
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection
The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets.
Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6
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 generators.
Discovering Sparse Representations of Lie Groups with Machine Learning
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators.
Oracle-Preserving Latent Flows
We develop a deep learning methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset.
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles
We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset.
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression
We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology.
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra
Transit spectroscopy is a powerful tool to decode the chemical composition of the atmospheres of extrasolar planets.
Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression
The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra which are obtained from complex numerical models of radiative transfer.
