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Attention module – this can be a dot product of recurrent states, or the query-key-value fully-connected layers. The output is a 100-long vector w. H 500×100. 100 hidden vectors h concatenated into a matrix c 500-long context vector = H * w. c is a linear combination of h vectors weighted by w.
A non-masked attention module can be thought of as a masked attention module where the mask has all entries zero. As an example of an uncommon use of mask matrix, the XLNet considers all masks of the form P M causal P − 1 {\displaystyle PM_{\text{causal}}P^{-1}} , where P {\displaystyle P} is a random permutation matrix .
Visual spatial attention is a form of visual attention that involves directing attention to a location in space. Similar to its temporal counterpart visual temporal attention , these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable explanation [ 1 ] [ 2 ...
Scaled dot-product attention & self-attention. The use of the scaled dot-product attention and self-attention mechanism instead of a Recurrent neural network or Long short-term memory (which rely on recurrence instead) allow for better performance as described in the following paragraph. The paper described the scaled-dot production as follows:
Attention is best described as the sustained focus of cognitive resources on information while filtering or ignoring extraneous information. Attention is a very basic function that often is a precursor to all other neurological/cognitive functions. As is frequently the case, clinical models of attention differ from investigation models.
Visual temporal attention is a special case of visual attention that involves directing attention to specific instant of time. Similar to its spatial counterpart visual spatial attention , these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable ...
A cross-attention module maps a (larger) byte array (e.g., a pixel array) and a latent array (smaller) to another latent array, reducing dimensionality. A transformer tower maps one latent array to another latent array, which is used to query the input again. The two components alternate. Both components use query-key-value (QKV) attention.
However, different definitions of "module" have been proposed by different authors. According to Jerry Fodor, the author of Modularity of Mind, a system can be considered 'modular' if its functions are made of multiple dimensions or units to some degree. [1] One example of modularity in the mind is binding. When one perceives an object, they ...