论文标题
转移学习，替代方法和可视化卷积神经网络，用于从光电子动量分布中检索分子中核对细胞内距离
Transfer learning, alternative approaches, and visualization of a convolutional neural network for retrieval of the internuclear distance in a molecule from photoelectron momentum distributions
论文作者
论文摘要
我们研究了深度学习在二维H $ _2^{+} $分子中从二维h $ _2^{+} $分子中的应用中的应用。我们研究了载体 - 内玻璃阶段对卷积神经网络预测核对间距离的影响。我们应用转移学习技术使我们的卷积神经网络适用于训练数据范围之外的参数获得的分布。将卷积神经网络与该问题的替代方法进行了比较，包括直接比较动量分布，支持向量机和决策树。发现这些替代方法具有非常有限的可传递性。最后，我们使用闭塞敏感技术来提取允许神经网络做出决定的功能。
We investigate the application of deep learning to the retrieval of the internuclear distance in the two-dimensional H$_2^{+}$ molecule from the momentum distribution of photoelectrons produced by strong-field ionization. We study the effect of the carrier-envelope phase on the prediction of the internuclear distance with a convolutional neural network. We apply the transfer learning technique to make our convolutional neural network applicable to distributions obtained for parameters outside the ranges of the training data. The convolutional neural network is compared with alternative approaches to this problem, including the direct comparison of momentum distributions, support-vector machines, and decision trees. These alternative methods are found to possess very limited transferability. Finally, we use the occlusion-sensitivity technique to extract the features that allow a neural network to take its decisions.
