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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 child concentrating on playing a video game, an example of using one's attention span to carry out a task. Attention span is the amount of time spent concentrating on a task before becoming distracted. [1] Distractibility occurs when attention is uncontrollably diverted to another activity or sensation. [2]
Writing in childhood is the process of developing writing abilities during the early years of life, generally from infancy to adolescence.Writing in childhood encompasses the growth of writing abilities, including acquiring skills to write letters and words, comprehending grammar and sentence structure, and cultivating the capacity to communicate ideas and feelings through written language ...
The Test of Everyday Attention (TEA) is designed to measure attention in adults age 18 through 80 years. The test comprises 8 subsets that represent everyday tasks and has three parallel forms. [ 1 ] It assess three aspects of attentional functioning: selective attention , sustained attention , and mental shifting .
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 .
These three networks have been studied using experimental designs involving adults, children, and monkeys, with and without abnormalities of attention. [4] Research designs include the Stroop task [5] and flanker task, which study executive control with analysis techniques including event-related functional magnetic resonance image (fMRI).
Some examples of cognitive modules: The modules controlling your hands when you ride a bike, to stop it from crashing, by minor left and right turns. The modules that allow a basketball player to accurately put the ball into the basket by tracking ballistic orbits. [7] The modules that recognise hunger and tell you that you need food. [8]
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: