Iclanzan, David and Szilágyi, László (2019) Learning to Generate Ambiguous Sequences. In: International Conference on Neural Information Processing - ICONIP2019, 12-15 Dec 2019, Sydney, Australia.
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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.
| Item Type: | Conference or Workshop Item (Paper) |
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| Uncontrolled Keywords: | neural networks, generative adversarial networks, objective ambiguity, Knightian uncertainty |
| Subjects: | Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány Q Science / természettudomány > QA Mathematics / matematika > QA76 Computer software / programozás |
| Depositing User: | Dr. László Szilágyi |
| Date Deposited: | 23 Sep 2020 13:33 |
| Last Modified: | 23 Sep 2020 13:33 |
| URI: | http://real.mtak.hu/id/eprint/114318 |
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