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The data is partitioned into volume bricks in object space, and the intermediate images are combined to a final picture in a sort-last approach.
The aim of this paper is to develop a general framework for training neural networks (NNs) in a distributed environment, where training data is partitioned over a set of agents that communicate with each other through a sparse, possibly time-varying, connectivity pattern.
When using both OW and RF links concurrently, data is partitioned between the links.
In -fold cross-validation, sample data is partitioned into folds, for.
The input data is partitioned into multiple blocks so that the data can be analyzed in parallel.
The input data is partitioned as 60%% data as training data set and 40%% testing data set.
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Diurnal heat-flux data were partitioned into daytime and nighttime, and positive and negative fractions.
Eight operation patterns have been identified and therefore the entire BAS data are partitioned into eight subsets.
For this analysis, data were partitioned into training and holdout sets (80% of observations used for training, 20% used as holdout) and relative sizes of positive samples were kept similar in both the sets (Supplementary Table 1).
These data were partitioned randomly into two sets, for training and testing.
The rows of data are partitioned based on their primary key.
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