The target measure is generated via a deterministic gradient descent algorithm, so as to match a set of statistics of the given, observed realization.
The covariance of a stationary process $X$ is diagonalized by a Fourier transform.
To approximate (interpolate) the marking function, in our baseline approach, we build a statistical regression model of the marks with respect some local point distance representation.
For wavelet filters, we show numerically that signals having sparse wavelet coefficients can be recovered from few phase harmonic correlations, which provide a compressive representation
Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood.
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.
We propose a new approach to linear ill-posed inverse problems.
Computational Engineering, Finance, and Science
Sparse scattering regressions give state of the art results over two databases of organic planar molecules.
We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation.
A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network.
This paper aims at presenting a new approach to the electro-sensing problem using wavelets.
A general framework for solving image inverse problems is introduced in this paper.