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Learning to Generate Ambiguous Sequences

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Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11953))

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Abstract

In this paper, we experiment with methods for obtaining binary sequences with a random probability mass function and with low autocorrelation and use it to generate ambiguous outcomes.

Outputs from a neural network are mixed and shuffled, resulting in binary sequences whose probability mass function is non-convergent, constantly moving and changing.

Empirical comparison with algorithms that generate ambiguity shows that the sequences generated by the proposed method have a significantly lower serial dependence. Therefore, the method is useful in scenarios where observes can see and record the outcome of each draw sequentially, by hindering the ability to make useful statistical inferences.

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Notes

  1. 1.

    https://github.com/HaskellAmbiguity/AmbiguityGenerator.

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Acknowledgments

This research was partially supported by Sapientia Foundation – Institute for Scientific Research (KPI). L. Szilágyi is János Bolyai Fellow of the Hungarian Academy of Sciences.

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Authors and Affiliations

  1. Sapientia University, Târgu-Mureş, Romania

    David Iclanzan & László Szilágyi

Authors
  1. David Iclanzan
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  2. László Szilágyi
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Corresponding author

Correspondence to David Iclanzan .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Iclanzan, D., Szilágyi, L. (2019). Learning to Generate Ambiguous Sequences. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-36708-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36707-7

  • Online ISBN: 978-3-030-36708-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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