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  1. Accompanies the paper "Learnability and Semantic Universals" ; trains recurrent neural networks to learn to verify sentences with quantifiers in order to explain semantic universals

    Python 10 8

  2. Accompanies the paper, "Ease of learning explains semantic universals". Generates and trains neural networks on artificial color systems, showing that _degree of convexity_ explains accuracy.

    Python 6 2

  3. Accompanies the paper, "An Explanation of the Veridical Uniformity Universal"; trains neural networks to learn so-called responsive verbs in order to explain a semantic universal.

    Python 1 1

  4. Accompanies the paper "Neural Models of the Psychosemantics of 'most'": trains neural models of visual attention to replicate varoius cognitive tasks

    Python 2

  5. Companion to the paper: Shane Steinert-Threlkeld, "Compositional Signaling in a Complex World", Journal of Logic, Language, and Information, vol 25 no 3, pp. 379-397 (DOI: 10.1007/s10849-016-9236-9)


  6. Code for a project on relation between scaling parameter in scale-free networks and size of protests

    R 1

332 contributions in the last year

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Contribution activity

February 2021

54 contributions in private repositories Feb 3 – Feb 26

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