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05/06/2023
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the feasibility of de...
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05/02/2023
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
PySR is an open-source library for practical symbolic regression, a type...
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11/24/2022
Learning Integrable Dynamics with Action-Angle Networks
Machine learning has become increasingly popular for efficiently modelli...
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11/15/2022
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
We propose parameterizing the population distribution of the gravitation...
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11/15/2022
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
We present a Bayesian graph neural network (BGNN) that can estimate the ...
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11/08/2022
A Neural Network Subgrid Model of the Early Stages of Planet Formation
Planet formation is a multi-scale process in which the coagulation of μ ...
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10/24/2022
: Learning Galaxy Properties from Merger Trees
Efficiently mapping baryonic properties onto dark matter is a major chal...
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09/05/2022
The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Ionized gas in the halo circumgalactic medium leaves an imprint on the c...
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07/25/2022
Automated discovery of interpretable gravitational-wave population models
We present an automatic approach to discover analytic population models ...
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07/18/2022
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a sta...
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02/28/2022
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks
Theoretical uncertainty limits our ability to extract cosmological infor...
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02/04/2022
Rediscovering orbital mechanics with machine learning
We present an approach for using machine learning to automatically disco...
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01/04/2022
Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter
Complex systems (stars, supernovae, galaxies, and clusters) often exhibi...
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12/31/2021
Learned Coarse Models for Efficient Turbulence Simulation
Turbulence simulation with classical numerical solvers requires very hig...
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06/17/2021
Unsupervised Resource Allocation with Graph Neural Networks
We present an approach for maximizing a global utility function by learn...
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03/22/2021
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
There is a shortage of multi-wavelength and spectroscopic followup capab...
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01/11/2021
A Bayesian neural network predicts the dissolution of compact planetary systems
Despite over three hundred years of effort, no solutions exist for predi...
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10/21/2020
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Supernovae mark the explosive deaths of stars and enrich the cosmos with...
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07/08/2020
Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way
We explore in this paper the use of neural networks designed for point-c...
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06/19/2020
Discovering Symbolic Models from Deep Learning with Inductive Biases
We develop a general approach to distill symbolic representations of a l...
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03/10/2020