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These equations allow us to calculate the portions of S that are distributed to representations a and b individually.
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APNNs have a multi-module, multi-level, and multi-modal design that works with an original scheme of sparse binary distributed representations to construct world models of varied complexity required for both task-specific and more general cognitive modeling.
Local lateral inhibition within each layer allows each local area within a layer to respond to and learn whatever is present in that local region independently of how much information and contrast there may be in other parts of a layer, and this, together with the non-linear activation function of the neurons, enables a sparse distributed representation to be produced.
For example, Joseph et al. [ 24] classified them into three categories: clustering-based, distributional and distributed word representations.
We present a model in which there is competition to activate distributed semantic representations.
Recently, many researchers have turned their attention to learning distributed text representations by neural networks (NNs) [6], e.g., convolutional NNs [9] and recursive NNs [23].
This self-reporting feature of distributed representations promises to resolve a philosophical conundrum about meaning.
Accordingly, a commitment to distributed representations should be considered a specific theoretical claim regarding the structure of knowledge rather than a core principle, as often assumed.
Unlike LDA, each word in an RSM is generated by multiple topics, leading to distributed and richer representations.
Hence, TMS to focal lip areas might have been able to cause stronger interference effects on the processing of incongruent words than TMS to the relatively distributed tongue representations.
Decoding distributed representations through the input representations is also possible.
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CEO of Professional Science Editing for Scientists @ prosciediting.com