Search Results for author: Stéphane Mallat

Found 29 papers, 15 papers with code

Scale Dependencies and Self-Similarity Through Wavelet Scattering Covariance

no code implementations19 Apr 2022 Rudy Morel, Gaspar Rochette, Roberto Leonarduzzi, Jean-Philippe Bouchaud, Stéphane Mallat

This covariance is nearly diagonalized by a second wavelet transform, which defines the scattering covariance.

Time Series

Generalized Rectifier Wavelet Covariance Models For Texture Synthesis

1 code implementation ICLR 2022 Antoine Brochard, Sixin Zhang, Stéphane Mallat

State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN).

Texture Synthesis

Phase Collapse in Neural Networks

1 code implementation ICLR 2022 Florentin Guth, John Zarka, Stéphane Mallat

Spatial variability is therefore transformed into variability along channels.

Tight Frame Contractions in Deep Networks

no code implementations ICLR 2021 John Zarka, Florentin Guth, Stéphane Mallat

Numerical experiments demonstrate that deep neural networks classifiers progressively separate class distributions around their mean, achieving linear separability.

Separation and Concentration in Deep Networks

2 code implementations18 Dec 2020 John Zarka, Florentin Guth, Stéphane Mallat

On the opposite, a soft-thresholding on tight frames can reduce within-class variabilities while preserving class means.

General Classification Image Classification

Particle gradient descent model for point process generation

1 code implementation27 Oct 2020 Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat, Sixin Zhang

The target measure is generated via a deterministic gradient descent algorithm, so as to match a set of statistics of the given, observed realization.

Point Processes Topological Data Analysis

Maximum Entropy Models from Phase Harmonic Covariances

2 code implementations22 Nov 2019 Sixin Zhang, Stéphane Mallat

The covariance of a stationary process $X$ is diagonalized by a Fourier transform.

Statistical learning of geometric characteristics of wireless networks

no code implementations19 Dec 2018 Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat, Sixin Zhang

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.

Point Processes

Phase Harmonic Correlations and Convolutional Neural Networks

2 code implementations29 Oct 2018 Stéphane Mallat, Sixin Zhang, Gaspar Rochette

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

Time Series

Generative networks as inverse problems with Scattering transforms

1 code implementation ICLR 2018 Tomás Angles, Stéphane Mallat

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.

Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties

no code implementations1 May 2018 Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stéphane Mallat, Louis Thiry

We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.

Multiscale Hierarchical Convolutional Networks

no code implementations12 Mar 2017 Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders

Multiscale hierarchical convolutional networks are structured deep convolutional networks where layers are indexed by progressively higher dimensional attributes, which are learned from training data.

Inverse Problems with Invariant Multiscale Statistics

no code implementations18 Sep 2016 Ivan Dokmanić, Joan Bruna, Stéphane Mallat, Maarten de Hoop

We propose a new approach to linear ill-posed inverse problems.

Computational Engineering, Finance, and Science

Wavelet Scattering Regression of Quantum Chemical Energies

1 code implementation16 May 2016 Matthew Hirn, Stéphane Mallat, Nicolas Poilvert

Sparse scattering regressions give state of the art results over two databases of organic planar molecules.

Understanding Deep Convolutional Networks

no code implementations19 Jan 2016 Stéphane Mallat

Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems.

Wavelet Scattering on the Pitch Spiral

1 code implementation3 Jan 2016 Vincent Lostanlen, Stéphane Mallat

We present a new representation of harmonic sounds that linearizes the dynamics of pitch and spectral envelope, while remaining stable to deformations in the time-frequency plane.

Quantum Energy Regression using Scattering Transforms

no code implementations6 Feb 2015 Matthew Hirn, Nicolas Poilvert, Stéphane Mallat

We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation.

Deep Roto-Translation Scattering for Object Classification

1 code implementation CVPR 2015 Edouard Oyallon, Stéphane Mallat

Dictionary learning algorithms or supervised deep convolution networks have considerably improved the efficiency of predefined feature representations such as SIFT.

Classification Dictionary Learning +3

Unsupervised Deep Haar Scattering on Graphs

no code implementations NeurIPS 2014 Xu Chen, Xiuyuan Cheng, Stéphane Mallat

The classification of high-dimensional data defined on graphs is particularly difficult when the graph geometry is unknown.

Classification Dimensionality Reduction +1

Rigid-Motion Scattering for Texture Classification

no code implementations7 Mar 2014 Laurent SIfre, Stéphane Mallat

A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network.

Classification General Classification +2

Generic Deep Networks with Wavelet Scattering

1 code implementation20 Dec 2013 Edouard Oyallon, Stéphane Mallat, Laurent SIfre

We introduce a two-layer wavelet scattering network, for object classification.

General Classification

Wavelet methods for shape perception in electro-sensing

no code implementations10 Oct 2013 Habib Ammari, Stéphane Mallat, Irène Waldspurger, Han Wang

This paper aims at presenting a new approach to the electro-sensing problem using wavelets.

Deep Learning by Scattering

no code implementations24 Jun 2013 Stéphane Mallat, Irène Waldspurger

We introduce general scattering transforms as mathematical models of deep neural networks with l2 pooling.

General Classification

Deep Scattering Spectrum

1 code implementation24 Apr 2013 Joakim andén, Stéphane Mallat

A scattering transform defines a locally translation invariant representation which is stable to time-warping deformations.

Sound Information Theory Information Theory

Invariant Scattering Convolution Networks

1 code implementation5 Mar 2012 Joan Bruna, Stéphane Mallat

A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification.

Classification General Classification +1

Classification with Scattering Operators

no code implementations12 Nov 2010 Joan Bruna, Stéphane Mallat

A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information.

Classification General Classification +4

Cannot find the paper you are looking for? You can Submit a new open access paper.