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Modeling Task fMRI Data Via Deep Convolutional Autoencoder., , , , , , , и . IEEE Trans. Medical Imaging, 37 (7): 1551-1561 (2018)Recognizing Brain States Using Deep Sparse Recurrent Neural Network., , , , , , , и . IEEE Trans. Medical Imaging, 38 (4): 1058-1068 (2019)Genetic Influence on Gyral Peaks., , , , , , , , , и . NeuroImage, (октября 2023)MadDroid: Characterising and Detecting Devious Ad Content for Android Apps., , , , , , , , и . CoRR, (2020)Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics., , , , и . Neurocomputing, (2019)Modeling Brain Diverse and Complex Hemodynamic Response Patterns via Deep Recurrent Autoencoder., , , , , , и . IEEE Trans. Cogn. Dev. Syst., 12 (4): 733-743 (2020)Latent source mining in FMRI data via deep neural network., , , , , , и . ISBI, стр. 638-641. IEEE, (2016)Multple-demand system identification and characterization via sparse representations of fMRI data., , , , и . ISBI, стр. 70-73. IEEE, (2016)Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)., , , , , , , и . MICCAI (3), том 11072 из Lecture Notes in Computer Science, стр. 181-189. Springer, (2018)Deformable Registration of Tumor-Diseased Brain Images., , и . MICCAI (1), том 3216 из Lecture Notes in Computer Science, стр. 720-728. Springer, (2004)