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Attention layers

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“Attention layers” refers to one or more computational layers within a neural network that implement an attention mechanism to assign contextual weights to elements of an input sequence. In one embodiment, attention layers are neural network components designed to compute contextual relevance between elements of input data. An attention layer may operate on token embeddings, feature vectors, hidden states, or the like, and compute weighted combinations of values based on learned relationships between query vectors, key vectors, and value vectors, or the like. Attention layers may be configured to perform self-attention, cross-attention, multi-head attention, or the like, and are commonly used in transformer-based architectures to capture dependencies between elements regardless of their position in a sequence. Attention layers may be stacked, combined with feed-forward layers, or integrated into encoder-decoder architectures to support tasks such as text generation, classification, summarization, or the like.