  Motivated by theories of language and communication that explain why
communities with large numbers of speakers have, on average, simpler languages
with more regularity, we cast the representation learning problem in terms of
learning to communicate. Our starting point sees the traditional autoencoder
setup as a single encoder with a fixed decoder partner that must learn to
communicate. Generalizing from there, we introduce community-based autoencoders
in which multiple encoders and decoders collectively learn representations by
being randomly paired up on successive training iterations. We find that
increasing community sizes reduce idiosyncrasies in the learned codes,
resulting in representations that better encode concept categories and
correlate with human feature norms.
