Residual Connection

Residual connections, which add the input of a layer to its output, are a fundamental architectural element in deep neural networks, primarily aimed at mitigating the vanishing gradient problem and improving training stability. Current research focuses on optimizing residual connections within various architectures, including Transformers, Graph Neural Networks (GNNs), and Spiking Neural Networks (SNNs), exploring variations like gated or weighted residuals and their impact on oversmoothing and representation learning. This ongoing work has significant implications for training deeper and more efficient models across diverse applications, from image processing and natural language processing to solving partial differential equations and medical image analysis.