====== Program Induction ======

  * [[https://arxiv.org/pdf/1608.04428.pdf|Gaunt 2016 - TerpreT: A Probabilistic Programming Language for Program Induction]] "The inference task is to observe a set of input-output examples and infer the underlying program... (we) automatically perform inference using four different back-ends that include machine learning and program synthesis approaches. These are based on gradient descent (thus each specification can be seen as a differentiable interpreter), linear program (LP) relaxations for graphical models, discrete satisfiability solving, and the Sketch program synthesis system."

===== Neural Program Induction =====
  * [[https://arxiv.org/pdf/1612.00712.pdf|Murray & Krishnamurthy 2016 - Probabilistic Neural Programs]]
  * [[https://arxiv.org/pdf/2007.03629.pdf|Li et al 2020 - Strong Generalization and Efficiency in Neural Programs]]  They need to compare to other program induction techniques.

===== Related Pages =====
  * [[nlp:Language to Programs]]