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09/14/2023
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Deep learning was recently successfully used in deriving symmetry transf...
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08/15/2023
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection
The next generation of telescopes will yield a substantial increase in t...
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07/10/2023
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 sy...
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02/10/2023
Discovering Sparse Representations of Lie Groups with Machine Learning
Recent work has used deep learning to derive symmetry transformations, w...
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02/02/2023
Oracle-Preserving Latent Flows
We develop a deep learning methodology for the simultaneous discovery of...
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01/13/2023
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles
We design a deep-learning algorithm for the discovery and identification...
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11/15/2022
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 for...
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10/04/2022
New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation
We propose an intuitive, machine-learning approach to multiparameter inf...
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01/07/2022
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra
Transit spectroscopy is a powerful tool to decode the chemical compositi...
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12/22/2021
Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression
The physical characteristics and atmospheric chemical composition of new...
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05/21/2021
Deep-Learned Event Variables for Collider Phenomenology
The choice of optimal event variables is crucial for achieving the maxim...
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08/31/2020