Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

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Co-consistent Regularization with Discriminative Feature for Zero-Shot Learningriminative feature extraction, we propose an end-to-end framework, which is different from traditional ZSL methods in the following two aspects: (1) we use a cascaded network to automatically locate discriminative regions, which can better extract latent features and contribute to the representation

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Hybrid Networks: Improving Deep Learning Networks via Integrating Two Views of Imagesata by transforming it into column vectors which destroys its spatial structure while obtaining the principal components. In this research, we first propose a tensor-factorization based method referred as the . (.). The . retains the spatial structure of the data by preserving its individual modes.

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On a Fitting of a Heaviside Function by Deep ReLU Neural Networksd an advantage of a deep structure in realizing a heaviside function in training. This is significant not only as simple classification problems but also as a basis in constructing general non-smooth functions. A heaviside function can be well approximated by a difference of ReLUs if we can set extr

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Efficient Integer Vector Homomorphic Encryption Using Deep Learning for Neural Networksosing users’ privacy when we train a high-performance model with a large number of datasets collected from users without any protection. To protect user privacy, we propose an Efficient Integer Vector Homomorphic Encryption (EIVHE) scheme using deep learning for neural networks. We use EIVHE to encr

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Multi-stage Gradient Compression: Overcoming the Communication Bottleneck in Distributed Deep Learniaining. Gradient compression is an effective way to relieve the pressure of bandwidth and increase the scalability of distributed training. In this paper, we propose a novel gradient compression technique, Multi-Stage Gradient Compression (MGC) with Sparsity Automatic Adjustment and Gradient Recessi

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