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One attractive theoretical idea, going back to the 1950s, is that these representations constructed by the brain are efficient in the sense of information theory [2] [4].
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The start and end points for each of these segments can be determined by using the same representations constructed in the present study.
Nor can it be explained by comparisons of visible object representations with internal representations constructed from tactile information because such explanations would predict similar levels of activation on both incongruent and congruent cross-modal trials.
The state space representation is constructed by unsupervised and competitive learning as well as the mapping state-action is constructed by means of reinforcement learning, during the maze exploration.
In the first approach, a PC representation is constructed by matching a target joint probability density function (pdf) based on sequential conditioning (a sequence of conditional probability relations) in conjunction with the Rosenblatt transformation.
As discussed in Section 1 of the article, attention is given to the functional representations of drivers, constructed "by" and "for" the action, such as they are implemented in a driving situation, and taking into account both implicit and explicit dimensions.
In the area of computer vision, multi-scale representations were first constructed by repeated smoothing and subsampling, leading to the notion of pyramids (Burt 1981; Crowley 1981; Burt and Adelson 1983; Crowley and Stern 1984; Crowley and Parker 1984; Crowley and Sanderson 1987).
Such a representation could be constructed by integrating lower-level, unisensory, viewpoint-dependent representations [16].
First of all, an extended assembly tolerance representation model is constructed by introducing a spatial relation layer.
From phenomenology it adopted the concept of the life-world, a representation of reality constructed by the intentions and functional categories of particular human subjects, and it studied these 'places' phenomenologically and semiotically.
The final representation of data constructed by the deep learning algorithm (output of the final layer) provides useful information from the data which can be used as features in building classifiers, or even can be used for data indexing and other applications which are more efficient when using abstract representations of data rather than high dimensional sensory data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com