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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.
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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 are continuously competing to be the momentary foci of attention. Selection models of attention theorize how specific stimuli gain our awareness.
The consciousness and binding problem is the problem of how objects, background, and abstract or emotional features are combined into a single experience. [1] The binding problem refers to the overall encoding of our brain circuits for the combination of decisions, actions, and perception.
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