35 citations · 143 across the 27 of their papers we have counts for
11 papers · 1 filter
Test like you Train in Implicit Deep Learning
Zaccharie Ramzi, Pierre Ablin, Gabriel Peyré +1
Implicit deep learning has recently gained popularity with applications ranging from meta-learning to Deep Equilibrium Networks (DEQs). In its general formulation, it relies on exp…
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy +4
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with…
Data augmentation for learning predictive models on EEG: a systematic comparison
Cédric Rommel, Joseph Paillard, Thomas Moreau +1
Objective: The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the re…
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…
Deep invariant networks with differentiable augmentation layers
Cédric Rommel, Thomas Moreau, Alexandre Gramfort
Designing learning systems which are invariant to certain data transformations is critical in machine learning. Practitioners can typically enforce a desired invariance on the trai…
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Cédric Rommel, Thomas Moreau, Joseph Paillard +1
Data augmentation is a key element of deep learning pipelines, as it informs the network during training about transformations of the input data that keep the label unchanged. Manu…
Understanding approximate and unrolled dictionary learning for pattern recovery
Benoît Malézieux, Thomas Moreau, Matthieu Kowalski
Dictionary learning consists of finding a sparse representation from noisy data and is a common way to encode data-driven prior knowledge on signals. Alternating minimization (AM)…
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
Zaccharie Ramzi, Florian Mannel, Shaojie Bai +3
In recent years, implicit deep learning has emerged as a method to increase the effective depth of deep neural networks. While their training is memory-efficient, they are still si…
NeuMiss networks: differentiable programming for supervised learning with missing values
Marine Le Morvan, Julie Josse, Thomas Moreau +2
The presence of missing values makes supervised learning much more challenging. Indeed, previous work has shown that even when the response is a linear function of the complete dat…
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals
Thomas Moreau, Alexandre Gramfort
Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to multidimensional data. CDL has proven useful for image denoising or inpainting, as well as for pa…
DICOD: Distributed Convolutional Sparse Coding
Thomas Moreau, Laurent Oudre, Nicolas Vayatis
In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals. This algorithm is designed to run in a dis…