5 citations · 14 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…