论文标题
探索神经网络训练策略以确定沮丧的磁模型中的相变
Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
论文作者
论文摘要
神经网络的转移学习是其最杰出的方面之一，并通过神经网络为监督学习是数据科学中的重要地位。在这里，我们在强烈相互作用的多体系统的背景下探索了此功能。通过案例研究，我们使用完全连接和卷积的神经网络测试了这种深度学习技术在沮丧的自旋系统中检测阶段及其过渡的潜力。此外，我们探索了一种最近引入的技术，该技术正处于受监督和无监督学习的中间。它包括评估在培训期间故意“混淆”的神经网络的性能。为了正确地证明“混乱”和传递学习技术的能力，我们将它们应用于二维中沮丧的磁性范式模型，以确定其相图并将其与高性能的蒙特卡洛模拟进行比较。
The transfer learning of a neural network is one of its most outstanding aspects and has given supervised learning with neural networks a prominent place in data science. Here we explore this feature in the context of strongly interacting many-body systems. Through case studies, we test the potential of this deep learning technique to detect phases and their transitions in frustrated spin systems, using fully-connected and convolutional neural networks. In addition, we explore a recently-introduced technique, which is at the middle point of supervised and unsupervised learning. It consists in evaluating the performance of a neural network that has been deliberately "confused" during its training. To properly demonstrate the capability of the "confusion" and transfer learning techniques, we apply them to a paradigmatic model of frustrated magnetism in two dimensions, to determine its phase diagram and compare it with high-performance Monte Carlo simulations.
