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Despite the gain of popularity of deep learning, it is very computation intensive and requires expensive hardware and large set of training data.
Even for linear elastic behavior, stress analysis of thick laminated composites can be very computation intensive if every lamina is modeled discretely.
Moreover, most of the above methods are based on probabilistic models, which are typically very computation intensive.
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It requires very little computation time: this approach comes down to compute and check linear inequalities at each sample time.
Therefore, it is possible to use the filters computed by the SIRT-FBP method in gridrec as well, which enables very efficient computation of SIRT-FBP reconstructions on both workstations [32] and large-scale supercomputers.
First, Surv-MDR requires very intensive computations by computing log-rank test statistics for all possible combinations of SNPs.
Further, it requires very little computation and memory.
Therefore, these projection methods have very similar computation speed.
Moreover, complex appearance models lead to very high computation.
Therefore, the enumeration method would be a very large computation, and not practicable.
However, these devices usually have very limited computation and memory resources, while URL-based filtering is quite demanding.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com