Aggregation functions play an important role in the message passing framework and the readout functions of Graph Neural Networks.

The dynamic edge convolutional operator from the "Dynamic Graph CNN for Learning on Point Clouds" paper (see torch_geometric. Applies the Softmin function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0, 1] and sum to 1.

Allows the model to jointly attend to information from different representation subspaces as described in the paper: Attention Is All You Need.

Applies a multi-layer Elman RNN with tanh ⁡ \tanh tanh or ReLU \text{ReLU} ReLU non-linearity to an input sequence. Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.Applies batch normalization over a batch of features as described in the "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper. Applies layer normalization over each individual example in a batch of features as described in the "Layer Normalization" paper. Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . In taking part in sporting, creative and fund-raising activities they develop their skills of leadership, creativity and awareness of the world around them.

InstanceNorm2d module with lazy initialization of the num_features argument of the InstanceNorm2d that is inferred from the input. The soft attention aggregation layer from the "Graph Matching Networks for Learning the Similarity of Graph Structured Objects" paper.

The DimeNet++ from the "Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules" paper. Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x x x and target y y y of size ( N , C ) (N, C) ( N , C ). The graph attentional propagation layer from the "Attention-based Graph Neural Network for Semi-Supervised Learning" paper.