Layer Normalisation — Technology Wiki

Overview

Direct Answer

Layer Normalisation is a technique that normalises the activations of a neural network by computing statistics (mean and variance) across the feature dimension for each individual sample, independent of batch composition. This differs from batch normalisation, which normalises across the batch dimension whilst preserving per-sample feature variation.

How It Works

For each sample, the algorithm computes the mean and standard deviation across all features in a given layer, then rescales activations using learnable affine parameters (gain and bias). The normalisation is applied independently to each sample, making it invariant to batch size and batch composition. This approach is particularly effective in recurrent and transformer architectures where temporal or sequential dependencies exist within samples.

Why It Matters

Layer normalisation stabilises training in models where batch statistics are unreliable or unavailable—notably recurrent neural networks, sequence-to-sequence models, and transformer-based architectures. It improves convergence speed, reduces sensitivity to initialisation, and enables robust performance across variable batch sizes, directly enhancing model robustness and training efficiency in production systems.

Common Applications

This technique is foundational in transformer models used for natural language processing, machine translation, and large language models. It is also employed in recurrent architectures for time-series forecasting, speech recognition systems, and reinforcement learning agents where batch normalisation is impractical or ineffective.

Key Considerations

Layer normalisation introduces additional computational overhead per sample and may be less effective than batch normalisation in fully-connected feedforward networks where batch statistics are stable. Performance characteristics vary significantly depending on architecture choice and problem domain, requiring empirical validation during model development.

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