Miles Cranmer | DeepAI

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Featured Co-authors

Rui Xu
53 publications
Kyle Cranmer
43 publications
Maurizio Pierini
37 publications
Shirley Ho
35 publications
Peter Battaglia
33 publications
Patrick Forré
32 publications
Can Cui
31 publications
Francisco Villaescusa-Navarro
21 publications
Alvaro Sanchez-Gonzalez
20 publications
Michael Eickenberg
19 publications
Brian Nord
15 publications

∙ 05/06/2023

Symbolic Regression on FPGAs for Fast Machine Learning Inference

The high-energy physics community is investigating the feasibility of de...

0 Ho Fung Tsoi, et al. ∙

∙ 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...

0 Miles Cranmer, et al. ∙

∙ 11/24/2022

Learning Integrable Dynamics with Action-Angle Networks

Machine learning has become increasingly popular for efficiently modelli...

0 Ameya Daigavane, et al. ∙

∙ 11/15/2022

Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study

We propose parameterizing the population distribution of the gravitation...

24 David Ruhe, et al. ∙

∙ 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 ...

7 Ji-won Park, et al. ∙

∙ 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 ...

0 Thomas Pfeil, et al. ∙

∙ 10/24/2022

: Learning Galaxy Properties from Merger Trees

Efficiently mapping baryonic properties onto dark matter is a major chal...

0 Christian Kragh Jespersen, et al. ∙

∙ 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...

0 Digvijay Wadekar, et al. ∙

∙ 07/25/2022

Automated discovery of interpretable gravitational-wave population models

We present an automatic approach to discover analytic population models ...

0 Kaze W. K. Wong, et al. ∙

∙ 07/18/2022

Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks

Simulation-based inference (SBI) is rapidly establishing itself as a sta...

16 Pablo Lemos, et al. ∙

∙ 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...

0 Leander Thiele, et al. ∙

∙ 02/04/2022

Rediscovering orbital mechanics with machine learning

We present an approach for using machine learning to automatically disco...

0 Pablo Lemos, et al. ∙

∙ 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...

0 Digvijay Wadekar, et al. ∙

∙ 12/31/2021

Learned Coarse Models for Efficient Turbulence Simulation

Turbulence simulation with classical numerical solvers requires very hig...

5 Kimberly Stachenfeld, et al. ∙

∙ 06/17/2021

Unsupervised Resource Allocation with Graph Neural Networks

We present an approach for maximizing a global utility function by learn...

0 Miles Cranmer, et al. ∙

∙ 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...

0 V. Ashley Villar, et al. ∙

∙ 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...

270 Miles Cranmer, et al. ∙

∙ 10/21/2020

Anomaly Detection for Multivariate Time Series of Exotic Supernovae

Supernovae mark the explosive deaths of stars and enrich the cosmos with...

0 V. Ashley Villar, et al. ∙

∙ 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...

12 Ademola Oladosu, et al. ∙

∙ 06/19/2020

Discovering Symbolic Models from Deep Learning with Inductive Biases

We develop a general approach to distill symbolic representations of a l...

12 Miles Cranmer, et al. ∙

∙ 03/10/2020

Lagrangian Neural Networks

Accurate models of the world are built upon notions of its underlying sy...

9 Miles Cranmer, et al. ∙

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