activity
20172020
most citedPredicting the long-term stability of compact multiplanet systems

108 citations · 141 across the 3 of their papers we have counts for

collaborators

5 papers

astro-ph.EP2020108 cited

Predicting the long-term stability of compact multiplanet systems

Daniel Tamayo, Miles Cranmer, Samuel Hadden +11

We combine analytical understanding of resonant dynamics in two-planet systems with machine learning techniques to train a model capable of robustly classifying stability in compac…

cs.LG2020

Lagrangian Neural Networks

Miles Cranmer, Sam Greydanus, Stephan Hoyer +3

Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet…

cs.LG201932 cited

Learning Symbolic Physics with Graph Networks

Miles D. Cranmer, Rui Xu, Peter Battaglia +1

We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization. Our expe…

astro-ph.IM2019

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates

Miles D. Cranmer, Richard Galvez, Lauren Anderson +2

We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable…

astro-ph.IM20171 cited

Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy

Miles D. Cranmer, Benjamin R. Barsdell, Danny C. Price +11

Radio astronomy observatories with high throughput back end instruments require real-time data processing. While computing hardware continues to advance rapidly, development of rea…