In this study we used a model based on deep architectures to provide better representations of Input.
Representations of input scalars, vectors, or compositional relational structures are constructed on-the-fly, so that similar items produce representations similar in terms of vector dot-products.
The space vector approach is based on the instantaneous space vector representation of input and output voltages and currents.
The space vector modulation is based on the instantaneous space vector representation of input and output voltage and current space vector as discussed in Figure 3.
On the other hand, NMF conducts a parts-based sparse representation where only a few components or bases are relevant for representation of input nonnegative matrix X.
Representation of input and output vectors.
Data integration and representation of input variables.
This subcellular representation of inputs is similar to, for example, the retinotopy that is present on a multi-neuron level in the mammalian primary visual cortex.
This study used a matrix representation with parsimony (MRP) analysis based on input trees generated from individual partitions and combinations of partitions with matrix elements weighted in proportion to bootstrap values of corresponding input tree clades.
Similar to textual data, Deep Learning can be used on other kinds of data to extract semantic representations from the input corpus, allowing for semantic indexing of that data.
