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LEARNING NON-DIFFERENTIABLE WEIGHTS OF NEURAL NETWORKS USING EVOLUTIONARY STRATEGIES

机译:使用进化策略学习非微弱的神经网络权重

摘要

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. The neural network has a plurality of differentiable weights and a plurality of non-differentiable weights. One of the methods includes determining trained values of the plurality of differentiable weights and the non-differentiable weights by repeatedly performing operations that include determining an update to the current values of the plurality of differentiable weights using a machine learning gradient-based training technique and determining, using an evolution strategies (ES) technique, an update to the current values of a plurality of distribution parameters.
机译:方法,系统和设备,包括在计算机存储介质上编码的计算机程序,用于训练神经网络。 神经网络具有多个可微分的权重和多个非可分性重量。 其中一个方法包括通过重复执行包括使用基于机器学习梯度的训练技术和确定的机器学习梯度的培训技术确定对多种微分权重的当前值的更新的操作来确定多个可微分权重和非微分权重的训练值。 ,使用演进策略(ES)技术,更新到多个分发参数的当前值。

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