  We aim to bridge the gap between our common-sense few-sample human learning
and large-data machine learning. We derive a theory of human-like few-shot
learning from von-Neuman-Landauer's principle. modelling human learning is
difficult as how people learn varies from one to another. Under commonly
accepted definitions, we prove that all human or animal few-shot learning, and
major models including Free Energy Principle and Bayesian Program Learning that
model such learning, approximate our theory, under Church-Turing thesis. We
find that deep generative model like variational autoencoder (VAE) can be used
to approximate our theory and perform significantly better than baseline models
including deep neural networks, for image recognition, low resource language
processing, and character recognition.
