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Theory II: Landscape of the Empirical Risk in Deep Learning, , , and . (2017)Compression of Deep Neural Networks for Image Instance Retrieval., , , , , , and . DCC, page 300-309. IEEE, (2017)Subtasks of Unconstrained Face Recognition., , and . VISAPP (2), page 113-121. SciTePress, (2014)Theory III: Dynamics and Generalization in Deep Networks., , , , , , and . CoRR, (2019)Biologically-Plausible Learning Algorithms Can Scale to Large Datasets., , , and . ICLR (Poster), OpenReview.net, (2019)Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review, , , , and . International Journal of Automation and Computing, (2017)Biologically-plausible learning algorithms can scale to large datasets., , , and . CoRR, (2018)Learning invariant representations and applications to face verification., , and . NIPS, page 3057-3065. (2013)Theory of Deep Learning III: explaining the non-overfitting puzzle., , , , , , , and . CoRR, (2018)Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization, , and . (2019)cite arxiv:1908.09375Comment: arXiv admin note: text overlap with arXiv:1611.00740.