Exact(10)
In all cases, five latent network structures yelled the optimal fits.
These results indicate that the five latent network structures were similar under different selections of ICA model order.
Results from individual subjects and the summarized results after brain map registration are shown for each latent network structure.
For each experimental condition (C + or C − trials), we determined the significant comparisons for each latent network structure by performing trial shuffling.
DOI: http://dx.doi.org/10.7554/eLife.06121.016 10.7554/eLiFigure21.017 Figure 3 figure supplement 3. Five latent network structures were robust against ICA model order selection.
We determined the interdependencies among latent network structures by examining how the activation of one structure could affect the activation of the others.
Similar(50)
This approach, called ensemble of GM-SMCC model (EGM-SMCC), constructs various kinds of latent networks with various latent linkages to explicitly model the relationships among the node.
We show how to effectively integrate these latent networks in an ensemble framework to improve the performance of protein function prediction.
To further boost the learning performance, we extend our proposed GM-SMCC method in an ensemble manner by constructing multiple latent networks.
To further model the stochastic properties arising from either latent variables affecting network dynamics or the uncertainty from model inference, we allow m vector-valued network functions, F = { f1, f2,..., f m }, to determine the expression states of genes in the network model through time.
Latent position cluster network models were used to characterise relationships.
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