Theo Gnassounou

2 accepted papers

2026

PSDNorm: Temporal Normalization for Deep Learning in Sleep Staging

Theo Gnassounou, Antoine Collas, Rémi Flamary, Alexandre Gramfort

ICLR 2026poster

Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and recording devices, such as sleep data. While existing normalization layers, BatchNorm, LayerNorm and InstanceNorm, h…

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2023

Convolution Monge Mapping Normalization for learning on sleep data

Theo Gnassounou, Rémi Flamary, Alexandre Gramfort

NeurIPS 2023poster

In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices. In this work, we propose a new method called Convolutional Monge Mapping Normalization ($\t…

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