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Convolutional neural networks typically consist of many convolutional layers followed by one or more fully connected layers.
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Tensor decompositions for learning latent variable models
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Sex differences in the structural connectome of the human brain
Madhura Ingalhalikar, Alex Smith, Drew Parker, Theodore D Satterthwaite, Mark A Elliott, Kosha Ruparel, Hakon Hakonarson, Raquel E Gur, Ruben C Gur, and Ragini Verma · 2014
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A. Cichocki, D. Mandic, L. De Lathauwer, G. Zhou, Q. Zhao, C. Caiafa, and H. A. PHAN · 2015
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Tensor contractions with extended blas kernels on cpu and gpu
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Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
James H Cole, Rudra PK Poudel, Dimosthenis Tsagkrasoulis, Matthan WA Caan, Claire Steves, Tim D Spector, and Giovanni Montana · 2017
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Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deep multi-task representation learning: A tensor factorisation approach
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Sharing residual units through collective tensor factorization to improve deep neural networks
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Brain age predicts mortality
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