Search Results for author: Stephane Mallat

Found 5 papers, 1 papers with code

The Quijote simulations

3 code implementations11 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

Multiscale Sparse Microcanonical Models

no code implementations6 Jan 2018 Joan Bruna, Stephane Mallat

Asymptotic properties of maximum entropy microcanonical and macrocanonical processes and their convergence to Gibbs measures are reviewed.

Gaussian Processes Point Processes +1

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

no code implementations NeurIPS 2017 Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stephane Mallat

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.

General Classification

Deep Haar Scattering Networks

no code implementations30 Sep 2015 Xiuyuan Cheng, Xu Chen, Stephane Mallat

An orthogonal Haar scattering transform is a deep network, computed with a hierarchy of additions, subtractions and absolute values, over pairs of coefficients.

Classification General Classification

Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination

no code implementations CVPR 2013 Laurent Sifre, Stephane Mallat

An affine invariant representation is constructed with a cascade of invariants, which preserves information for classification.

Classification General Classification +1

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