  This paper proposes a Fast Graph Convolutional Neural Network (FGRNN)
architecture to predict sequences with an underlying graph structure. The
proposed architecture addresses the limitations of the standard recurrent
neural network (RNN), namely, vanishing and exploding gradients, causing
numerical instabilities during training. State-of-the-art architectures that
combine gated RNN architectures, such as Long Short-Term Memory (LSTM) and
Gated Recurrent Unit (GRU) with graph convolutions are known to improve the
numerical stability during the training phase, but at the expense of the model
size involving a large number of training parameters. FGRNN addresses this
problem by adding a weighted residual connection with only two extra training
parameters as compared to the standard RNN. Numerical experiments on the real
3D point cloud dataset corroborates the proposed architecture.
