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Accompanies the paper "Learnability and Semantic Universals" ; trains recurrent neural networks to learn to verify sentences with quantifiers in order to explain semantic universals
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.
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.
Accompanies the paper "Neural Models of the Psychosemantics of 'most'": trains neural models of visual attention to replicate varoius cognitive tasks
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)
Code for a project on relation between scaling parameter in scale-free networks and size of protests
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