论文标题
Riccinets：使用RICCI流量的曲率引导的高性能神经网络修剪
RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow
论文作者
论文摘要
在提出训练之前，一种新的方法，用于识别随机有线神经网络中的显着计算路径。根据局部图测量定义的节点质量概率函数，并由基于增强学习的控制器神经网络产生的高参数加权，将计算图修剪。在将计算图映射到神经网络之前，我们使用RICCI曲率的定义来删除重要性的边缘。我们显示的每次通过的浮点操作数量（FLOP）的数量降低了近35美元\％$，而且性能没有降解。此外，我们的方法可以基于纯粹的结构属性成功地使随机有线的神经网络正规化，并且发现一个网络中确定的有利特性推广到其他网络。该方法产生的网络在压缩的压缩下与最低降压重量所修剪的网络相似。据我们所知，这是修剪随机有线神经网络的第一部作品，也是第一个在修剪机制中利用RICCI曲率拓扑度量的方法。
A novel method to identify salient computational paths within randomly wired neural networks before training is proposed. The computational graph is pruned based on a node mass probability function defined by local graph measures and weighted by hyperparameters produced by a reinforcement learning-based controller neural network. We use the definition of Ricci curvature to remove edges of low importance before mapping the computational graph to a neural network. We show a reduction of almost $35\%$ in the number of floating-point operations (FLOPs) per pass, with no degradation in performance. Further, our method can successfully regularize randomly wired neural networks based on purely structural properties, and also find that the favourable characteristics identified in one network generalise to other networks. The method produces networks with better performance under similar compression to those pruned by lowest-magnitude weights. To our best knowledge, this is the first work on pruning randomly wired neural networks, as well as the first to utilize the topological measure of Ricci curvature in the pruning mechanism.
