Layer Normalization

Layer normalization (LN) is a technique used in deep neural networks to stabilize training and improve performance by normalizing the activations of neurons within a layer. Current research focuses on understanding LN's geometric properties, its interaction with other normalization methods (like RMSNorm and Batch Normalization), and its impact on model stability and efficiency, particularly within transformer architectures and various applications such as natural language processing and image generation. These investigations aim to optimize LN's implementation, potentially leading to more efficient and robust deep learning models across diverse domains.