Titlebook: Neural Information Processing; 24th International C Derong Liu,Shengli Xie,El-Sayed M. El-Alfy Conference proceedings 2017 Springer Interna

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Demulcent

Jizhao Zhu,Jianzhong Qiao,Xinxiao Dai,Xueqi Chengmanagerial efforts due to seemingly never-ending user requirements that certainly add to complexity of project management. We emphasized the importance of progress, operationalized as a function of size and fault. Moreover, it is important that the progress be reported regularly and timely which nec

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Pcos971

Rustem Takhanov,Zhenisbek Assylbekovprovement. All software projects encounter software faults during development and have to put much effort into locating and fixing these. A lot of information is produced when handling faults, through fault reports. This paper reports a study of fault reports from industrial projects, where we seek

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Tree-Structure CNN for Automated Theorem Provingn this paper, we present a novel neural network, which can effectively help people to finish this work. Specifically, we design a tree-structure CNN, involving bidirectional LSTM. We compare our model with other neural network models and make experiments on HOLStep dataset, which is a machine learni

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Training Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connectionsre reuse; that is, features are ‘diluted’ as they are forward propagated through the model. Hence, later network layers receive less informative signals about the input data, consequently making training less effective. In this work, we address the problem of feature reuse by taking inspiration from

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Knowledge Memory Based LSTM Model for Answer Selectioned to enhance the information interaction between questions and answers, knowledge is still the gap between their representations. In this paper, we propose a knowledge memory based RNN model, which incorporates the knowledge learned from the data sets into the question representations. Experiments

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