论文标题
将增强拓扑结合的神经进化与卷积神经网络相结合
Combining Neuro-Evolution of Augmenting Topologies with Convolutional Neural Networks
论文作者
论文摘要
当前的深卷卷网络在其拓扑中固定。我们通过将增强拓扑的神经进化（NEAT）与卷积神经网络（CNN）相结合，并使用残留网络块（Resnets）提出这样一个系统，从而探索使卷积拓扑的可能性成为参数本身。然后，我们解释只有在进行其他优化后才能构建建议的系统，因为遗传算法比每个反向传播的培训都要要求更高。在此途中，我们解释了大多数流行语，并为机器学习的最重要的现代领域提供了温和，简短的介绍
Current deep convolutional networks are fixed in their topology. We explore the possibilites of making the convolutional topology a parameter itself by combining NeuroEvolution of Augmenting Topologies (NEAT) with Convolutional Neural Networks (CNNs) and propose such a system using blocks of Residual Networks (ResNets). We then explain how our suggested system can only be built once additional optimizations have been made, as genetic algorithms are way more demanding than training per backpropagation. On the way there we explain most of those buzzwords and offer a gentle and brief introduction to the most important modern areas of machine learning
