两篇新研究论文探讨了神经网络和迁移学习在处理高维伊辛模型中的应用。第一篇论文研究了各种神经网络架构的分布外性能，发现基于Transformer的模型和卷积神经网络采用了不同的统计策略，这些策略可能导致表观鲁棒性，但并非真正的物理规则学习。第二篇论文介绍了Trans-Ising，一种旨在通过有效利用辅助数据集来改进伊辛模型估计的迁移学习方法，与仅使用目标数据集的方法相比，其估计误差更低。 AI
影响 这些研究突出了将神经网络应用于科学发现中潜在的陷阱，并提出了使用迁移学习来提高模型性能的方法。
排序理由 两篇关于机器学习应用于统计物理模型的新arXiv论文。
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arXiv:2607.03039v1 Announce Type: new Abstract: Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects transferable physical rule learning. We address this…
arXiv:2607.03005v1 Announce Type: cross Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging. To address this, we propose Trans-Ising, a transfer learn…
In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging. To address this, we propose Trans-Ising, a transfer learning method that combines a loss-based source scree…