1 code implementation • 19 Aug 2022 • Valentina Salvatelli, Luiz F. G. dos Santos, Souvik Bose, Brad Neuberg, Mark C. M. Cheung, Miho Janvier, Meng Jin, Yarin Gal, Atilim Gunes Baydin
The Solar Dynamics Observatory (SDO), a NASA multi-spectral decade-long mission that has been daily producing terabytes of observational data from the Sun, has been recently used as a use-case to demonstrate the potential of machine learning methodologies and to pave the way for future deep-space mission planning.
no code implementations • 5 Mar 2020 • Michael D. Himes, Joseph Harrington, Adam D. Cobb, Atilim Gunes Baydin, Frank Soboczenski, Molly D. O'Beirne, Simone Zorzan, David C. Wright, Zacchaeus Scheffer, Shawn D. Domagal-Goldman, Giada N. Arney
Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy.
Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics
1 code implementation • 10 Nov 2019 • Valentina Salvatelli, Souvik Bose, Brad Neuberg, Luiz F. G. dos Santos, Mark Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin
The synergy between machine learning and this enormous amount of data has the potential, still largely unexploited, to advance our understanding of the Sun and extend the capabilities of heliophysics missions.
2 code implementations • 10 Nov 2019 • Brad Neuberg, Souvik Bose, Valentina Salvatelli, Luiz F. G. dos Santos, Mark Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites, the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since2010.
no code implementations • pproximateinference AABI Symposium 2019 • Bradley Gram-Hansen, Christian Schroeder de Witt, Robert Zinkov, Saeid Naderiparizi, Adam Scibior, Andreas Munk, Frank Wood, Mehrdad Ghadiri, Philip Torr, Yee Whye Teh, Atilim Gunes Baydin, Tom Rainforth
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
no code implementations • 8 Nov 2018 • Frank Soboczenski, Michael D. Himes, Molly D. O'Beirne, Simone Zorzan, Atilim Gunes Baydin, Adam D. Cobb, Yarin Gal, Daniel Angerhausen, Massimo Mascaro, Giada N. Arney, Shawn D. Domagal-Goldman
Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3, 000, 000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator.
no code implementations • 21 Dec 2017 • Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data.
3 code implementations • ICLR 2018 • Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio, Mark Schmidt, Frank Wood
We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice.
no code implementations • 2 Mar 2017 • Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank Wood
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning.
4 code implementations • 31 Oct 2016 • Tuan Anh Le, Atilim Gunes Baydin, Frank Wood
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods.
4 code implementations • 20 Feb 2015 • Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning.
no code implementations • 30 Apr 2014 • Atilim Gunes Baydin, Ramon Lopez de Mantaras, Santiago Ontanon
We introduce a novel evolutionary algorithm (EA) with a semantic network-based representation.
no code implementations • 28 Apr 2014 • Atilim Gunes Baydin, Barak A. Pearlmutter
Automatic differentiation---the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately---dates to the origin of the computer age.