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Theory II: Landscape of the Empirical Risk in Deep Learning, , , and . (2017)How Important is Weight Symmetry in Backpropagation?, , and . CoRR, (2015)Compression of Deep Neural Networks for Image Instance Retrieval., , , , , , and . DCC, page 300-309. IEEE, (2017)Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization, , and . (2019)cite arxiv:1908.09375Comment: arXiv admin note: text overlap with arXiv:1611.00740.Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning., , and . CoRR, (2016)Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex., and . CoRR, (2016)Theory of Deep Learning III: explaining the non-overfitting puzzle., , , , , , , and . CoRR, (2018)When and Why Are Deep Networks Better Than Shallow Ones?, , and . AAAI, page 2343-2349. AAAI Press, (2017)Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review., , , , and . Int. J. Autom. Comput., 14 (5): 503-519 (2017)Subtasks of Unconstrained Face Recognition., , and . VISAPP (2), page 113-121. SciTePress, (2014)