scholarly journals Learning State Space Trajectories in Recurrent Neural Networks: A preliminary Report

Author(s):  
Barak A. Pearlmutter
2016 ◽  
Vol 39 ◽  
Author(s):  
Stefan L. Frank ◽  
Hartmut Fitz

AbstractPrior language input is not lost but integrated with the current input. This principle is demonstrated by “reservoir computing”: Untrained recurrent neural networks project input sequences onto a random point in high-dimensional state space. Earlier inputs can be retrieved from this projection, albeit less reliably so as more input is received. The bottleneck is therefore not “Now-or-Never” but “Sooner-is-Better.”


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