提出向量信号张量积的积分公式,加速3到9倍计算
Integral Formulas for Vector Signal Tensor Products
- 推导出反称化盖恩特系数的闭式表达式
- 单次张量积可替代克莱布什-戈丹运算,提速最高9倍
- 适合做旋转对称神经网络的高效实现
我们推导出张量积的积分公式,简化了由谢等人提出的向量信号张量积,该方法将盖恩特张量积推广至反对称耦合情形。特别地,我们获得了反对称盖恩特系数的显式闭式表达式。这使得仅用一次向量信号张量积即可模拟克莱布什-戈丹张量积,使所需张量积计算量最多减少9倍。结果为向量信号张量积的高效实用实现铺平道路,推动其在SO(3)等变神经网络中的应用。此外,我们讨论了盖恩特与向量信号张量积如何控制通常克莱布什-戈丹张量积的表达能力与运行时间权衡。最后,我们研究了所考虑张量积归一化项的低秩分解,以适用于等变神经网络。
原文摘要 · Abstract (English)
We derive integral formulas that simplify the Vector Signal Tensor Product recently introduced by Xie et al., which generalizes the Gaunt tensor product to anti-symmetric couplings. In particular, we obtain explicit closed-form expressions for the anti-symmetric analogues of the Gaunt coefficients. This enables us to simulate the Clebsch-Gordan tensor product using a single Vector Signal Tensor Product, yielding up to a $9\times$ reduction in the required tensor product evaluations. Our results enable efficient and practical implementations of the Vector Signal Tensor Product, paving the way for applications of this generalization of Gaunt Tensor Products in $\mathrm{SO}(3)$-equivariant neural networks. Moreover, we discuss how the Gaunt and the Vector Signal Tensor Products allow to control the expressivity-runtime tradeoff associated with the usual Clebsch-Gordan Tensor Products. Finally, we investigate low rank decompositions of the normalizations of the considered tensor products in view of their use in equivariant neural networks.
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