论文标题
传播神经网络：从病毒扩散模型到神经网络
Transmission Neural Networks: From Virus Spread Models to Neural Networks
论文作者
论文摘要
这项工作将病毒在网络上传播的模型与其等效的神经网络表示。基于此连接，我们提出了一种新的神经网络体系结构，称为传输神经网络（Transnns），其中激活功能主要与链接相关，并允许具有不同的激活水平。此外，这种连接导致具有可调或可训练参数的三个新激活函数的发现和推导。此外，我们证明具有单个隐藏层和固定的非零偏置项的Transns是通用函数近似器。最后，我们提出了基于Transnn的连续时间流行网络模型的新基本派生。
This work connects models for virus spread on networks with their equivalent neural network representations. Based on this connection, we propose a new neural network architecture, called Transmission Neural Networks (TransNNs) where activation functions are primarily associated with links and are allowed to have different activation levels. Furthermore, this connection leads to the discovery and the derivation of three new activation functions with tunable or trainable parameters. Moreover, we prove that TransNNs with a single hidden layer and a fixed non-zero bias term are universal function approximators. Finally, we present new fundamental derivations of continuous time epidemic network models based on TransNNs.
