Project Summery

In this project, I have undertaken the implementation of a model that marries the Transformer-Encoder architecture with the concept of Graph Convolution. This innovative fusion of ideas draws inspiration from the GConv-GRU model, which is a novel development in combining graph convolutions with RNN models.

While GConv-GRU adeptly combined Graph Convolution with the Gated Recurrent Unit (GRU), I have chosen to integrate the powerful Transformer-Encoder architecture into this framework.

This novel approach demonstrates the project's originality and highlights the quest for enhanced performance and adaptability within the realm of deep learning and neural network design.