Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Bahri, Mehdi"'
Autor:
Wei, Xiaoxi, Faisal, A. Aldo, Grosse-Wentrup, Moritz, Gramfort, Alexandre, Chevallier, Sylvain, Jayaram, Vinay, Jeunet, Camille, Bakas, Stylianos, Ludwig, Siegfried, Barmpas, Konstantinos, Bahri, Mehdi, Panagakis, Yannis, Laskaris, Nikolaos, Adamos, Dimitrios A., Zafeiriou, Stefanos, Duong, William C., Gordon, Stephen M., Lawhern, Vernon J., Śliwowski, Maciej, Rouanne, Vincent, Tempczyk, Piotr
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 because current methods cannot generalise well across human subjects' dat
Externí odkaz:
http://arxiv.org/abs/2202.12950
Autor:
Bakas, Stylianos, Ludwig, Siegfried, Barmpas, Konstantinos, Bahri, Mehdi, Panagakis, Yannis, Laskaris, Nikolaos, Adamos, Dimitrios A., Zafeiriou, Stefanos
Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our approach to address this difficulty is to explicitly align feature distri
Externí odkaz:
http://arxiv.org/abs/2202.03267
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 vectorization, or on iterative graph construction using next move prediction.
Externí odkaz:
http://arxiv.org/abs/2112.05215
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 grids to arbitrary topologies, GNNs also bring much of the implementa
Externí odkaz:
http://arxiv.org/abs/2012.15823
Autor:
Bahri, Mehdi, Sullivan, Eimear O', Gong, Shunwang, Liu, Feng, Liu, Xiaoming, Bronstein, Michael M., Zafeiriou, Stefanos
Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inf
Externí odkaz:
http://arxiv.org/abs/2012.09235
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 the desire to design more powerful architectures, often by adapting e
Externí odkaz:
http://arxiv.org/abs/2004.02658
Dictionary learning and component analysis models are fundamental for learning compact representations that are relevant to a given task (feature extraction, dimensionality reduction, denoising, etc.). The model complexity is encoded by means of spec
Externí odkaz:
http://arxiv.org/abs/1801.06432
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, and statistical machine learning. In dictionary learning, the current
Externí odkaz:
http://arxiv.org/abs/1703.07886
Autor:
Bahri, Mehdi1 (AUTHOR) m.bahri@imperial.ac.uk, O' Sullivan, Eimear1 (AUTHOR), Gong, Shunwang1 (AUTHOR), Liu, Feng2 (AUTHOR), Liu, Xiaoming2 (AUTHOR), Bronstein, Michael M.1 (AUTHOR), Zafeiriou, Stefanos1 (AUTHOR)
Publikováno v:
International Journal of Computer Vision. Sep2021, Vol. 129 Issue 9, p2680-2713. 34p.
Publikováno v:
CVPR 2022 – IEEE Conference on Computer Vision and Pattern Recognition EarthVision Workshop
CVPR 2022 – IEEE Conference on Computer Vision and Pattern Recognition EarthVision Workshop, Jun 2022, New Orleans (Louisiana), United States
CVPR 2022 – IEEE Conference on Computer Vision and Pattern Recognition EarthVision Workshop, Jun 2022, New Orleans (Louisiana), United States
International audience; 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 vectorization, or on iterative graph construction usin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76ba2e6928cb3e919161336ea7b6ea76
https://doi.org/10.1109/cvprw56347.2022.00146
https://doi.org/10.1109/cvprw56347.2022.00146