异构神经网络的降维：广义多项式混沌（gPC）和变异分析（ANOVA）
Dimension reduction in heterogeneous neural networks: generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)
摘要：
我们提出，并说明通过神经网络的例子，两种不同的方法来粗粒度的大型异构网络。这两种方法的灵感，并使用开发的工具，不确定性量化的方法在系统中的多个不确定参数-在我们的情况下，参数是不均匀分布在网络节点。该方法在加速大规模网络模拟以及粗粒度不动点、周期解和稳定性分析方面显示出了良好的前景。我们还表明，该方法可以成功地处理结构以及内在的异质性。
英文摘要：
We propose, and illustrate via a neural network example, two different approaches to coarse-graining large heterogeneous networks. Both approaches are inspired from, and use tools developed in, methods for uncertainty quantification in systems with multiple uncertain parameters - in our case, the parameters are heterogeneously distributed on the network nodes. The approach shows promise in accelerating large scale network simulations as well as coarse-grained fixed point, periodic solution and stability analysis. We also demonstrate that the approach can successfully deal with structural as well as intrinsic heterogeneities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
