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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.
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.
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
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 ...
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]
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.
Crossmodal attention refers to the distribution of attention to different senses. Attention is the cognitive process of selectively emphasizing and ignoring sensory stimuli. According to the crossmodal attention perspective, attention often occurs simultaneously through multiple sensory modalities . [ 1 ]