347 citations · 684 across the 26 of their papers we have counts for
4 papers · 1 filter
Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering
Aaqib Saeed, David Grangier, Olivier Pietquin +1
We propose CHARM, a method for training a single neural network across inconsistent input channels. Our work is motivated by Electroencephalography (EEG), where data collection pro…
Contrastive Learning of General-Purpose Audio Representations
Aaqib Saeed, David Grangier, Neil Zeghidour
We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio. Our approach is based on contrastive learning: it learns a repres…
Sense and Learn: Self-Supervision for Omnipresent Sensors
Aaqib Saeed, Victor Ungureanu, Beat Gfeller
Learning general-purpose representations from multisensor data produced by the omnipresent sensing systems (or IoT in general) has numerous applications in diverse use cases. Exist…
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Aaqib Saeed, Flora D. Salim, Tanir Ozcelebi +1
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to p…