Search Results for author: Michael Eickenberg

Found 20 papers, 8 papers with code

Exploring the Optimality of Tight-Frame Scattering Networks

no code implementations29 Sep 2021 Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Muawiz Sajjad Chaudhary, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf

The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet.

Frame

Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning

no code implementations11 Jun 2021 Eugene Belilovsky, Louis Leconte, Lucas Caccia, Michael Eickenberg, Edouard Oyallon

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.

Image Classification Quantization

Practical Phase Retrieval: Low-Photon Holography with Untrained Priors

no code implementations1 Jan 2021 Hannah Lawrence, David Barmherzig, Henry Li, Michael Eickenberg, Marylou Gabrié

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 with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging

1 code implementation14 Dec 2020 Hannah Lawrence, David A. Barmherzig, Henry Li, Michael Eickenberg, Marylou Gabrié

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).

Decoupled Greedy Learning of CNNs

1 code implementation ICML 2020 Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

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.

Image Classification

Greedy Layerwise Learning Can Scale to ImageNet

1 code implementation29 Dec 2018 Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks.

Image Classification

Shallow Learning For Deep Networks

no code implementations27 Sep 2018 Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks.

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.

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

FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging

no code implementations22 Dec 2015 Gaël Varoquaux, Michael Eickenberg, Elvis Dohmatob, Bertand Thirion

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.

Brain Decoding

Data-driven HRF estimation for encoding and decoding models

no code implementations27 Feb 2014 Fabian Pedregosa, Michael Eickenberg, Philippe Ciuciu, Bertrand Thirion, Alexandre Gramfort

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 order scattering descriptors predict fMRI activity due to visual textures

no code implementations10 Aug 2013 Michael Eickenberg, Fabian Pedregosa, Senoussi Mehdi, Alexandre Gramfort, Bertrand Thirion

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.

General Classification

HRF estimation improves sensitivity of fMRI encoding and decoding models

no code implementations13 May 2013 Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort

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.

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