BSc AI Thesis: Chaotic Dynamic in the HTR model using Reservoir Computing
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2022-06-20
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en
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Abstract
Reservoir computing is a promising field of research in recurrent neural
networks. RC has multiple benefits compared to normal RNN’s such as
biological plausibility, accuracy, and others. The reservoirs of the network
are randomly generated and only a linear output layer is trained. However,
there is not much information about how the reservoirs process information
and how the parameters influence the results. This research intends
to explain the effect of the spectral radius and how ratios between layers in
the HTR model influence the performance on a dynamical system. This
will be done by implementing the HTR model using reservoir computing
on a hierarchical time series task (POS tagging). By experimenting
with slower and faster dynamics throughout the layers, the hypothesis
that the spectral radius needs to increase while retaining the ’effective
spectral radius’ has shown to be relatively incorrect. Although respecting
the ’effective spectral radius’ is important, the spectral radius is of little
influence and does not have significant impact on the results, only when
the ’effective spectral radius’ is violated, results become error prone.
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Faculteit der Sociale Wetenschappen