Pinned

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 10 8

2. color-learning

   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. responsive-verbs

   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. neural-vision-most

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

   Python 2

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)

   Python

6. scale-free-protest

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

   R 1