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      Learning deep physiological models of affect

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          Approximate entropy as a measure of system complexity.

          Techniques to determine changing system complexity from data are evaluated. Convergence of a frequently used correlation dimension algorithm to a finite value does not necessarily imply an underlying deterministic model or chaos. Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes. The capability to discern changing complexity from such a relatively small amount of data holds promise for applications of ApEn in a variety of contexts.
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            Extracting and composing robust features with denoising autoencoders

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              A unified architecture for natural language processing

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                Author and article information

                Journal
                IEEE Computational Intelligence Magazine
                IEEE Comput. Intell. Mag.
                Institute of Electrical and Electronics Engineers (IEEE)
                1556-603X
                May 2013
                May 2013
                : 8
                : 2
                : 20-33
                Article
                10.1109/MCI.2013.2247823
                2952ab3c-a132-403a-a4cc-978dc7d977bc
                © 2013
                History

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