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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.
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.
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.
The structure contains a high proportion of multisensory neurons and plays a role in the motor control of orientation behaviours of the eyes, ears and head. [ 53 ] Receptive fields from somatosensory, visual and auditory modalities converge in the deeper layers to form a two-dimensional multisensory map of the external world.
Feature integration theory is a theory of attention developed in 1980 by Anne Treisman and Garry Gelade that suggests that when perceiving a stimulus, features are "registered early, automatically, and in parallel, while objects are identified separately" and at a later stage in processing.
Attention can be guided by top-down processing or via bottom up processing. Posner's model of attention includes a posterior attentional system involved in the disengagement of stimuli via the parietal cortex, the shifting of attention via the superior colliculus and the engagement of a new target via the pulvinar. The anterior attentional ...
The scarcity of attention is the underlying assumption for attention management; the researcher Herbert A. Simon pointed out that when there is a vast availability of information, attention becomes the more scarce resource as human beings cannot digest all the information. [6] Fundamentally, attention is limited by the processing power of the ...
Additional research proposes the notion of a moveable filter. The multimode theory of attention combines physical and semantic inputs into one theory. Within this model, attention is assumed to be flexible, allowing different depths of perceptual analysis. [28] Which feature gathers awareness is dependent upon the person's needs at the time. [3]