32 citations · 36 across the 2 of their papers we have counts for
3 papers
Learning to learn generative programs with Memoised Wake-Sleep
Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum
We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs. As generative model…
Learning to Infer Program Sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum +1
Our goal is to build systems which write code automatically from the kinds of specifications humans can most easily provide, such as examples and natural language instruction. The…
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Luke B. Hewitt, Maxwell I. Nye, Andreea Gane +2
Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. How…