Technical

Deep Neural Networks are usually robust to hyper parameter choices. The training will be reasonably good with various reasonable combinations of hyper parameters. However, Generative Adversarial Neural Networks(GANN) do not follow this pattern. GANNs are used to generate synthetic data, such as real looking fake images. I won’t go deep into what a GANN is right now. I’ll provide some practical facts. Training such networks require specific hyper parameters and architectural choices. ...

I was reading up on Graph Neural Networks(GNN) recently. The way researchers formulated the GNN architectures is really interesting. The easiest way to understand GNNs is to compare it with CNN. Consequently, there is an architecture called GCNN or Graph Convolutional Neural Networks. GNNs and CNNs share some common motivation like, using shared parameters. Without shared parameters, CNN and GNNs both would need infeasible amount of parameters. CNNs solve this problem by exploiting the local spatial correlation of images. GNNs solve this problem using a node’s adjacency matrix. ...