Layer Normalization: Faster Training and Steady Neural States

Layer normalization is a technique that adjusts values across a whole layer for each single example, making neural network training smoother and quicker. It removes the need for batch dependence and helps models learn faster with more stable hidden states.

Details

Layer normalization is a method that helps neural networks learn faster by adjusting the values across a whole layer for each single example. This keeps the internal signals 'calm' and makes training smoother and quicker. Unlike batch normalization, layer normalization works the same way during training and real-world use, as it does not rely on groups of examples. This makes models less sensitive to how data is fed in. The technique also helps maintain stable hidden states over time, which is particularly useful for recurrent networks that process information step-by-step. Layer normalization applies a small shift and scale to each neuron, preserving the network's power while keeping it calm and consistent.