Thomas Moreau | DeepAI

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∙ 03/10/2023

Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

When dealing with electro or magnetoencephalography records, many superv...

0 Clément Bonet, et al. ∙

∙ 02/17/2023

A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization

Bilevel optimization problems, which are problems where two optimization...

0 Mathieu Dagréou, et al. ∙

∙ 10/10/2022

FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels

Temporal point processes (TPP) are a natural tool for modeling event-bas...

0 Guillaume Staerman, et al. ∙

∙ 06/29/2022

Data augmentation for learning predictive models on EEG: a systematic comparison

The use of deep learning for electroencephalography (EEG) classification...

0 Cédric Rommel, et al. ∙

∙ 06/10/2022

PAVI: Plate-Amortized Variational Inference

Given some observed data and a probabilistic generative model, Bayesian ...

0 Louis Rouillard, et al. ∙

∙ 02/04/2022

Deep invariant networks with differentiable augmentation layers

Designing learning systems which are invariant to certain data transform...

0 Cédric Rommel, et al. ∙

∙ 01/31/2022

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms

Bilevel optimization, the problem of minimizing a value function which i...

6 Mathieu Dagréou, et al. ∙

∙ 12/08/2021

DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals

The quantitative analysis of non-invasive electrophysiology signals from...

0 Cédric Allain, et al. ∙

∙ 06/25/2021

CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals

Data augmentation is a key element of deep learning pipelines, as it inf...

0 Cédric Rommel, et al. ∙

∙ 06/11/2021

Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems

Inverse problems consist in recovering a signal given noisy observations...

0 Benoît Malézieux, et al. ∙

∙ 06/01/2021

SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models

In recent years, implicit deep learning has emerged as a method to incre...

0 Zaccharie Ramzi, et al. ∙

∙ 02/12/2021

Leveraging Global Parameters for Flow-based Neural Posterior Estimation

Inferring the parameters of a stochastic model based on experimental obs...

6 Pedro L. C. Rodrigues, et al. ∙

∙ 10/19/2020

Learning to solve TV regularized problems with unrolled algorithms

Total Variation (TV) is a popular regularization strategy that promotes ...

3 Hamza Cherkaoui, et al. ∙

∙ 07/03/2020

Neumann networks: differential programming for supervised learning with missing values

The presence of missing values makes supervised learning much more chall...

11 Marine Le Morvan, et al. ∙

∙ 02/10/2020

Super-efficiency of automatic differentiation for functions defined as a minimum

In min-min optimization or max-min optimization, one has to compute the ...

13 Pierre Ablin, et al. ∙

∙ 05/27/2019

Learning step sizes for unfolded sparse coding

Sparse coding is typically solved by iterative optimization techniques, ...

8 Pierre Ablin, et al. ∙

∙ 01/26/2019

Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals

Convolutional dictionary learning (CDL) estimates shift invariant basis ...

52 Thomas Moreau, et al. ∙

∙ 05/24/2018

Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals

Frequency-specific patterns of neural activity are traditionally interpr...

2 Tom Dupré La Tour, et al. ∙

∙ 06/02/2017

Understanding the Learned Iterative Soft Thresholding Algorithm with matrix factorization

Sparse coding is a core building block in many data analysis and machine...

0 Thomas Moreau, et al. ∙

∙ 05/29/2017

Distributed Convolutional Sparse Coding

We consider the problem of building shift-invariant representations for ...

0 Thomas Moreau, et al. ∙

∙ 11/14/2016

Post Training in Deep Learning with Last Kernel

One of the main challenges of deep learning methods is the choice of an ...

0 Thomas Moreau, et al. ∙

∙ 09/01/2016

Understanding Trainable Sparse Coding via Matrix Factorization

Sparse coding is a core building block in many data analysis and machine...

0 Thomas Moreau, et al. ∙

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