5 citations · 8 across the 4 of their papers we have counts for
5 papers
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas +5
Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our app…
Binary Graph Neural Networks
Mehdi Bahri, Gaétan Bahl, Stefanos Zafeiriou
Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on…
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
Mehdi Bahri, Eimear O' Sullivan, Shunwang Gong +4
Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches…
Geometrically Principled Connections in Graph Neural Networks
Shunwang Gong, Mehdi Bahri, Michael M. Bronstein +1
Graph convolution operators bring the advantages of deep learning to a variety of graph and mesh processing tasks previously deemed out of reach. With their continued success comes…