189 citations · 582 across the 33 of their papers we have counts for
36 papers
Review of Machine Learning Models for Solar Energetic Particle Prediction
Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman +73
Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond…
Probabilistic Forecasting of Radiation Exposure for Spaceflight
Rutuja Gurav, Elena Massara, Xiaomei Song +5
Extended human presence beyond low-Earth orbit (BLEO) during missions to the Moon and Mars will pose significant challenges in the near future. A primary health risk associated wit…
Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods
Michael D. Himes, Joseph Harrington, Atilim Gunes Baydin
Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-r…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
Domain Invariant Representation Learning with Domain Density Transformations
A. Tuan Nguyen, Toan Tran, Yarin Gal +1
Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively tr…
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic +12
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of dilig…
Technology Readiness Levels for AI & ML
Alexander Lavin, Gregory Renard
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence…
Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer
Michael D. Himes, Joseph Harrington, Adam D. Cobb +8
Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach…
Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning
Brad Neuberg, Souvik Bose, Valentina Salvatelli +6
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites,the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since2010. Ultraviolet (UV) an…
Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona
Valentina Salvatelli, Souvik Bose, Brad Neuberg +6
Understanding and monitoring the complex and dynamic processes of the Sun is important for a number of human activities on Earth and in space. For this reason, NASA's Solar Dynamic…
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses
Anna Jungbluth, Xavier Gitiaux, Shane A. Maloney +7
Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on…
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
Kara Lamb, Garima Malhotra, Athanasios Vlontzos +7
High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes. These interactions o…
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
Kara Lamb, Garima Malhotra, Athanasios Vlontzos +7
A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weath…
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi, Adam Ścibior, Andreas Munk +9
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampli…
Flood Detection On Low Cost Orbital Hardware
Gonzalo Mateo-Garcia, Silviu Oprea, Lewis Smith +5
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much t…
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji +14
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remai…
Hijacking Malaria Simulators with Probabilistic Programming
Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth +3
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria. However, the complicated and stocha…
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Adam D. Cobb, Michael D. Himes, Frank Soboczenski +7
Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the pote…
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
Frank Soboczenski, Michael D. Himes, Molly D. O'Beirne +8
Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet…
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji +12
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which all…
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from po…
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov +1
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a…
Online Learning Rate Adaptation with Hypergradient Descent
Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio +2
We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness…
Tricks from Deep Learning
Atılım Güneş Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind
The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlin…