35 citations · 143 across the 27 of their papers we have counts for
3 papers · 2 filters
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…