论文标题
关于神经网络安排中学习能力的丧失
On the loss of learning capability inside an arrangement of neural networks
论文作者
论文摘要
我们分析了神经网络安排内的信息丢失和学习能力的丧失。我们的方法是新的，并基于非单身bogoliubov转换的制定，以便在安排的不同点之间连接信息。这可以在扩展傅立叶系列中扩展激活函数后完成，然后假设其信息存储在量子标量字段中。
We analyze the loss of information and the loss of learning capability inside an arrangement of neural networks. Our method is new and based on the formulation of non-unitary Bogoliubov transformations in order to connect the information between different points of the arrangement. This can be done after expanding the activation function in a Fourier series and then assuming that its information is stored inside a Quantum scalar field.
