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t-BNE: Tensor-based Brain Network Embedding., , , , , , и . SDM, стр. 189-197. SIAM, (2017)Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease., , , , , , , , , и . ISBI, стр. 1088-1092. IEEE, (2016)Probabilistic multi-tensor estimation using the Tensor Distribution Function., , , , , , и . CVPR, IEEE Computer Society, (2008)Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning., , , , , и . ECML/PKDD (3), том 10536 из Lecture Notes in Computer Science, стр. 228-240. Springer, (2017)Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health., , , , , , , , , и 5 other автор(ы). CHI, стр. 326:1-326:15. ACM, (2023)NeuroCave: A Web-based Immersive Visualization Platform for Exploring Connectome Datasets, , , , и . Network Neuroscience, (февраля 2018)Mapping Alzheimer's disease progression in 1309 MRI scans: Power estimates for different inter-scan intervals., , , , , , , , , и 5 other автор(ы). NeuroImage, 51 (1): 63-75 (2010)Federated Multi-view Learning for Private Medical Data Integration and Analysis., , , , , , и . ACM Trans. Intell. Syst. Technol., 13 (4): 61:1-61:23 (2022)Phase Angle Spatial Embedding (PhASE) - A Kernel Method for Studying the Topology of the Human Functional Connectome., , , , , , , и . MICCAI (3), том 11072 из Lecture Notes in Computer Science, стр. 367-374. Springer, (2018)Normative Modeling Via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease., , , , , и . ISBI, стр. 1-4. IEEE, (2023)