no code implementations • 6 May 2024 • Etienne Lempereur, Stéphane Mallat

An outstanding issue is to also approximate non-Gaussian fields having long-range interactions in space and across scales.

1 code implementation • 4 Oct 2023 • Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli, Stéphane Mallat

Finally, we show that when trained on regular image classes for which the optimal basis is known to be geometry-adaptive and harmonic, the denoising performance of the networks is near-optimal.

1 code implementation • 3 Aug 2023 • Rudy Morel, Stéphane Mallat, Jean-Philippe Bouchaud

We introduce a Path Shadowing Monte-Carlo method, which provides prediction of future paths, given any generative model.

no code implementations • 29 Jun 2023 • Sihao Cheng, Rudy Morel, Erwan Allys, Brice Ménard, Stéphane Mallat

In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics.

1 code implementation • 31 May 2023 • Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat

There is a growing gap between the impressive results of deep image generative models and classical algorithms that offer theoretical guarantees.

no code implementations • 29 May 2023 • Florentin Guth, Brice Ménard, Gaspar Rochette, Stéphane Mallat

Gaussian rainbow networks are defined with Gaussian weight distributions.

1 code implementation • 6 Mar 2023 • Zahra Kadkhodaie, Florentin Guth, Stéphane Mallat, Eero P Simoncelli

We instantiate this model using convolutional neural networks (CNNs) with local receptive fields, which enforce both the stationarity and Markov properties.

no code implementations • 11 Jul 2022 • Tanguy Marchand, Misaki Ozawa, Giulio Biroli, Stéphane Mallat

We develop a multiscale approach to estimate high-dimensional probability distributions from a dataset of physical fields or configurations observed in experiments or simulations.

3 code implementations • 19 Apr 2022 • Rudy Morel, Gaspar Rochette, Roberto Leonarduzzi, Jean-Philippe Bouchaud, Stéphane Mallat

We introduce the wavelet scattering spectra which provide non-Gaussian models of time-series having stationary increments.

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).

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

Spatial variability is therefore transformed into variability along channels.

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.

2 code implementations • 18 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.

2 code implementations • 27 Oct 2020 • Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat, Sixin Zhang

This paper presents a statistical model for stationary ergodic point processes, estimated from a single realization observed in a square window.

1 code implementation • 22 Nov 2019 • Sixin Zhang, Stéphane Mallat

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

1 code implementation • ICLR 2020 • John Zarka, Louis Thiry, Tomás Angles, Stéphane Mallat

It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence.

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, Muawiz Chaudhary, 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.

no code implementations • 19 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.

1 code implementation • 29 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

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.

no code implementations • 1 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.

no code implementations • 12 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.

no code implementations • 18 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

1 code implementation • 16 May 2016 • Matthew Hirn, Stéphane Mallat, Nicolas Poilvert

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

no code implementations • 19 Jan 2016 • Stéphane Mallat

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

1 code implementation • 3 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.

no code implementations • 6 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.

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.

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.

no code implementations • 7 Mar 2014 • Laurent SIfre, Stéphane Mallat

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

1 code implementation • 20 Dec 2013 • Edouard Oyallon, Stéphane Mallat, Laurent SIfre

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

no code implementations • 10 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.

no code implementations • 24 Jun 2013 • Stéphane Mallat, Irène Waldspurger

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

1 code implementation • 24 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

1 code implementation • 5 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.

no code implementations • 12 Nov 2010 • Joan Bruna, Stéphane Mallat

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

no code implementations • 15 Jun 2010 • Guoshen Yu, Guillermo Sapiro, Stéphane Mallat

A general framework for solving image inverse problems is introduced in this paper.

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