从结构特征推断卷积神经网络的精度
Inferring Convolutional Neural Networks' accuracies from their architectural characterizations
摘要：
卷积神经网络（CNN）在分析包括粒子成像探测器在内的许多领域的科学数据方面表现出了强大的前景。然而，选择适当的网络架构（深度、内核形状、激活函数等）的挑战是复杂的。对于具体的应用和不同的数据集仍然知之甚少。在本文中，我们研究了CNN的架构和它的性能之间的关系，提出了一个系统的语言，是有用的不同的CNN的架构之间的比较训练时间之前。我们通过不同的属性来表征CNN的体系结构，并证明这些属性可以预测两个特定的基于计算机视觉的物理问题-事件顶点发现和费米国家加速器实验室的MINERvA实验中的强子多重性分类中的网络性能。在此过程中，我们从优化的网络架构中提取了几个架构属性用于物理问题，这些属性是称为深度学习多节点进化神经网络（MENNDL）的模型选择算法的输出。我们使用机器学习模型来预测网络在训练之前是否可以表现得比某个阈值精度更好。这些模型的性能比随机猜测好16-20%。此外，我们发现普通最小二乘模型在大量网络的准确性回归中的决定系数为0.966。
英文摘要：
Convolutional Neural Networks (CNNs) have shown strong promise for analyzing scientific data from many domains including particle imaging detectors. However, the challenge of choosing the appropriate network architecture (depth, kernel shapes, activation functions, etc.) for specific applications and different data sets is still poorly understood. In this paper, we study the relationships between a CNN's architecture and its performance by proposing a systematic language that is useful for comparison between different CNN's architectures before training time. We characterize CNN's architecture by different attributes, and demonstrate that the attributes can be predictive of the networks' performance in two specific computer vision-based physics problems -- event vertex finding and hadron multiplicity classification in the MINERvA experiment at Fermi National Accelerator Laboratory. In doing so, we extract several architectural attributes from optimized networks' architecture for the physics problems, which are outputs of a model selection algorithm called Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL). We use machine learning models to predict whether a network can perform better than a certain threshold accuracy before training. The models perform 16-20% better than random guessing. Additionally, we found an coefficient of determination of 0.966 for an Ordinary Least Squares model in a regression on accuracy over a large population of networks.
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