1 code implementation • 22 Sep 2021 • Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, Leander Thiele, Romeel Dave, Desika Narayanan, Andrina Nicola, Yin Li, Pablo Villanueva-Domingo, Benjamin Wandelt, David N. Spergel, Rachel S. Somerville, Jose Manuel Zorrilla Matilla, Faizan G. Mohammad, Sultan Hassan, Helen Shao, Digvijay Wadekar, Michael Eickenberg, Kaze W. K. Wong, Gabriella Contardo, Yongseok Jo, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Lucia A. Perez, Daisuke Nagai, Nicholas Battaglia, Mark Vogelsberger
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.
1 code implementation • 22 Mar 2021 • V. Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory.
no code implementations • 21 Oct 2020 • V. Ashley Villar, Miles Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements.
1 code implementation • 1 Oct 2020 • Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N. Spergel, Rachel S. Somerville, Romeel Dave, Annalisa Pillepich, Lars Hernquist, Dylan Nelson, Paul Torrey, Desika Narayanan, Yin Li, Oliver Philcox, Valentina La Torre, Ana Maria Delgado, Shirley Ho, Sultan Hassan, Blakesley Burkhart, Digvijay Wadekar, Nicholas Battaglia, Gabriella Contardo
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
1 code implementation • 8 Jul 2020 • Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example.
no code implementations • 3 Jul 2020 • Wolfgang E. Kerzendorf, Christian Vogl, Johannes Buchner, Gabriella Contardo, Marc Williamson, Patrick van der Smagt
We show that we can train an emulator for this problem given a modest training set of a hundred thousand spectra (easily calculable on modern supercomputers).
1 code implementation • 17 Oct 2019 • Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro, Siyu He, Shy Genel, Shirley Ho
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
3 code implementations • 11 Sep 2019 • Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara, Arka Banerjee, Ana Maria Delgado, Doogesh Kodi Ramanah, Tom Charnock, Elena Giusarma, Yin Li, Erwan Allys, Antoine Brochard, Chi-Ting Chiang, Siyu He, Alice Pisani, Andrej Obuljen, Yu Feng, Emanuele Castorina, Gabriella Contardo, Christina D. Kreisch, Andrina Nicola, Roman Scoccimarro, Licia Verde, Matteo Viel, Shirley Ho, Stephane Mallat, Benjamin Wandelt, David N. Spergel
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
1 code implementation • 15 Feb 2019 • Xinyue Zhang, Yanfang Wang, Wei zhang, Yueqiu Sun, Siyu He, Gabriella Contardo, Francisco Villaescusa-Navarro, Shirley Ho
In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.
no code implementations • 26 Jun 2017 • Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
More specifically, we consider a pool-based setting, where the system observes all the examples of the dataset of a problem and has to choose the subset of examples to label in a single shot.
no code implementations • 13 Jul 2016 • Gabriella Contardo, Ludovic Denoyer, Thierry Artières
We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost.
no code implementations • 22 Dec 2014 • Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
Representations for both users and items are computed from the observed ratings and used for prediction.
no code implementations • 20 Dec 2013 • Gabriella Contardo, Ludovic Denoyer, Thierry Artieres, Patrick Gallinari
We propose to deal with sequential processes where only partial observations are available by learning a latent representation space on which policies may be accurately learned.