5 citations · 10 across the 5 of their papers we have counts for
8 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…
Road Extraction from Overhead Images with Graph Neural Networks
Gaetan Bahl, Mehdi Bahri, Florent Lafarge
Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectoriz…
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…
Robust Kronecker Component Analysis
Mehdi Bahri, Yannis Panagakis, Stefanos Zafeiriou
Dictionary learning and component analysis models are fundamental for learning compact representations that are relevant to a given task (feature extraction, dimensionality reducti…
Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling
Mehdi Bahri, Yannis Panagakis, Stefanos Zafeiriou
Dictionary learning and component analysis are part of one of the most well-studied and active research fields, at the intersection of signal and image processing, computer vision,…