no code implementations • 4 Jan 2022 • Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar, Lucia A. Perez, Pablo Villanueva-Domingo, Digvijay Wadekar, Helen Shao, Faizan G. Mohammad, Sultan Hassan, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Andrina Nicola, Leander Thiele, Yongseok Jo, Oliver H. E. Philcox, Benjamin D. Oppenheimer, Megan Tillman, ChangHoon Hahn, Neerav Kaushal, Alice Pisani, Matthew Gebhardt, Ana Maria Delgado, Joyce Caliendo, Christina Kreisch, Kaze W. K. Wong, William R. Coulton, Michael Eickenberg, Gabriele Parimbelli, Yueying Ni, Ulrich P. Steinwandel, Valentina La Torre, Romeel Dave, Nicholas Battaglia, Daisuke Nagai, David N. Spergel, Lars Hernquist, Blakesley Burkhart, Desika Narayanan, Benjamin Wandelt, Rachel S. Somerville, Greg L. Bryan, Matteo Viel, Yin Li, Vid Irsic, Katarina Kraljic, Mark Vogelsberger
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet.
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.
The wavelet scattering transform creates geometric invariants and deformation stability.
With the use of a replay buffer we show that this approach can be extended to asynchronous settings, where modules can operate and continue to update with possibly large communication delays.
To the best of our knowledge, this is the first work to consider a dataset-free machine learning approach for holographic phase retrieval.
Phase retrieval is the inverse problem of recovering a signal from magnitude-only Fourier measurements, and underlies numerous imaging modalities, such as Coherent Diffraction Imaging (CDI).
It is based on a greedy relaxation of the joint training objective, recently shown to be effective in the context of Convolutional Neural Networks (CNNs) on large-scale image classification.
Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks.
2 code implementations • 28 Dec 2018 • Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.
We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals.
The total variation (TV) penalty, as many other analysis-sparsity problems, does not lead to separable factors or a proximal operatorwith a closed-form expression, such as soft thresholding for the $\ell\_1$ penalty.
Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets.
Statistical machine learning methods are increasingly used for neuroimaging data analysis.
We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels.
Second layer scattering descriptors are known to provide good classification performance on natural quasi-stationary processes such as visual textures due to their sensitivity to higher order moments and continuity with respect to small deformations.
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal.