Incorporating inductive biases into ML models is an active area of ML research, especially when ML models are applied to data about the physical world. Equivariant Graph Neural Networks (GNNs) have recently become a popular method for learning from physics data because they directly incorporate the symmetries of the underlying physical system. Drawing from the relevant literature around group equivariant networks, this paper presents a comprehensive evaluation of the proposed benefits of equivariant GNNs by using real-world particle physics reconstruction tasks as an evaluation test-bed. We demonstrate that many of the theoretical benefits generally associated with equivariant networks may not hold for realistic systems and introduce compelling directions for future research that will benefit both the scientific theory of ML and physics applications.

翻译：将归纳偏置引入机器学习模型是机器学习研究的一个活跃领域，尤其是在将机器学习模型应用于物理世界数据时。等变图神经网络（GNN）最近已成为从物理数据中学习的一种流行方法，因为它们直接融入了底层物理系统的对称性。本文借鉴了关于群等变网络的相关文献，通过使用真实的粒子物理重建任务作为评估测试平台，对等变图神经网络所提出的优势进行了全面评估。我们证明，许多通常与等变网络相关的理论优势可能并不适用于真实系统，并提出了令人信服的未来研究方向，这些方向将有益于机器学习的科学理论以及物理应用。