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  • Institute for Logic, Language, and Computation
  • Universiteit van Amsterdam

Pinned repositories

  1. quantifier-rnn-learning

    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 1 4

  2. responsive-verbs

    Explaining semantic universals for so-called responsive verbs in terms of their learnability.


  3. scale-free-protest

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

    Python 1

  4. color-learning

    Learning of different artificial color systems, represented as partitions of a space

    Python 1

  5. learning-evolution-compositionality

    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. neural-vision-most

    Training neural models of visual attention to replicate varoius cognitive tasks

    Python 1

602 contributions in the last year

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Contribution activity

December 2018

2 contributions in private repositories Dec 7 – Dec 12

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