Neural networks for computing best rank-one approximations of tensors and its applications

报告题目：Neural networks for computing best rank-one    approximations of tensors and its applications

报告人：复旦大学 魏益民教授

报告时间：2017年11月8日（星期三）下午14：30--15：30

报告地点：上海师范大学（徐汇校区）10号楼222会议室

报告摘要：This talk presents the neural dynamical network to compute a best rank-one approximation of a real- valued tensor. We implement the neural network model by the ordinary differential equations (ODE), which is a class of continuous-time recurrent neural network. Several new properties of solutions for the neural network are established. We prove that the locally asymptotic stability of solutions for ODE by constructive an appropriate Lyapunov function under mild conditions.

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