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Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks
The results are demonstrated on short time series with lengths (L = 32, 64, 128) from continuous and discrete dynamical systems in chaotic regimes, nonlinear transform of linearly correlated noise and experimental data.
On Identifying Significant Edges in Graphical Models of Molecular Networks
Results: The improved performance of the proposed approach is demonstrated across the synthetic data sets using sensitivity, specificity and accuracy as performance metrics.
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