Alexander L. Gaunt

Summary - TerpreT: A Probabilistic Programming Language for Program Induction

2016 · arXiv (Cornell University)

We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our key contribution is TerpreT, a domain-specific language for expressing program synthesis …

Differentiable Programs with Neural Libraries

2016 · arXiv (Cornell University)

We develop a framework for combining differentiable programming languages with neural networks. Using this framework we create end-to-end trainable systems that learn to write interpretable algorithms with perceptual components. We explore the benefits of inductive …