论文标题
E3NN：欧几里得神经网络
e3nn: Euclidean Neural Networks
论文作者
论文摘要
我们提出了E3NN，这是一个通用框架，用于创建E（3）e术训练功能，也称为欧几里得神经网络。 E3NN自然地在几何图形和几何张量上进行操作，这些几何和几何张量描述了3D中的系统，并在坐标系统的变化下可预测地转换。 E3NN的核心是诸如Tensorproduct类或球形谐波函数之类的等效操作，这些功能可以组成，以创建更复杂的模块，例如卷积和注意机制。 E3NN的这些核心操作可用于有效地阐明张量球场网络，3D可通道的CNN，Clebsch-Gordan Networks，SE（3）变压器和其他E（3）E（3）Equivariant网络。
We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. The core of e3nn are equivariant operations such as the TensorProduct class or the spherical harmonics functions that can be composed to create more complex modules such as convolutions and attention mechanisms. These core operations of e3nn can be used to efficiently articulate Tensor Field Networks, 3D Steerable CNNs, Clebsch-Gordan Networks, SE(3) Transformers and other E(3) equivariant networks.
