This example demonstrates the use of Gated Residual Networks (GRN) and Variable Selection Networks (VSN), proposed by Bryan Lim et al. in Temporal Fusion Transformers (TFT) for Interpretable Multi-horizon Time Series Forecasting, for structured data classification. GRNs give the flexibility to the model to apply non-linear processing only where needed. VSNs allow the model to softly remove any unnecessary noisy inputs which could negatively impact performance. Together, those techniques help improving the learning capacity of deep neural network models.