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Multi-head attention enhances this process by introducing multiple parallel attention heads. Each attention head learns different linear projections of the Q, K, and V matrices. This allows the model to capture different aspects of the relationships between words in the sequence simultaneously, rather than focusing on a single aspect.
Concretely, let the multiple attention heads be indexed by , then we have (,,) = [] ((,,)) where the matrix is the concatenation of word embeddings, and the matrices ,, are "projection matrices" owned by individual attention head , and is a final projection matrix owned by the whole multi-headed attention head.
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text.
Multiheaded_attention,_block_diagram.png (656 × 600 pixels, file size: 32 KB, MIME type: image/png) This is a file from the Wikimedia Commons . Information from its description page there is shown below.
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The dummy head is designed to record multiple sounds at the same time enabling it to be exceptional at recording music as well as in other industries where multiple sound sources are involved. The dummy head is designed to replicate an average-sized human head and depending on the manufacturer may have a nose and mouth too.
Voluntary attention, otherwise known as top-down attention, is the aspect over which we have control, enabling us to act in a goal-directed manner. [14] In contrast, reflexive attention is driven by exogenous stimuli redirecting our current focus of attention to a new stimulus, thus it is a bottom-up influence. These two divisions of attention ...
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