35 citations · 143 across the 27 of their papers we have counts for
5 papers · 1 filter
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
Cédric Allain, Alexandre Gramfort, Thomas Moreau
The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal…
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…
HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe +1
Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is stron…