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An efficient representation structure is necessary to conceptualise anatomical space of an image to guide the mapping process.
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A DNN has much more modelling power than linear transforms due to its nonlinear activation function and deep layered structure, allowing efficient representation of complex mapping [22].
We explicitly include the hierarchy structure in our DSs because it is a natural way of defining composite objects, a more efficient structure for retrieval, and the representation structure used in MPEG-4.
Therefore, in this paper, we propose a new structure and an efficient representation of constrained association rule set based on closed itemsets and their generators.
Furthermore, the deep structure of DNNs allows for a more efficient representation of many nonlinear transformations due to many layers of simple nonlinear processing.
The advantage of a DNN over one-hidden-layer NN is that the deep structure of the DNN allows much more efficient representation of many nonlinear transformations/functions [22].
The efficient representation of visual information [1], [2] requires matching of the coding strategy of neurons to the statistical structure of their stimuli so that the information carried by neural responses is maximized [2], [4], [11], [12].
Efficient Representation and Validation of Proofs George C. Necula, Peter Lee.
Efficient representation and rendering with Spherical Harmonic Reflection Maps.
Efficient representation of images can be achieved by transforms.
Pagès, H. BSgenome: software infrastructure for efficient representation of full genomes and their SNPs.
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