326 citations · 928 across the 7 of their papers we have counts for
7 papers
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
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 k…
Differentiable Programs with Neural Libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman +1
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 inte…
DeepCoder: Learning to Write Programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt +2
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to pr…
Differentiable Functional Program Interpreters
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt +1
Programming by Example (PBE) is the task of inducing computer programs from input-output examples. It can be seen as a type of machine learning where the hypothesis space is the se…
Emergence of a Turbulent Cascade in a Quantum Gas
Nir Navon, Alexander L. Gaunt, Robert P. Smith +1
In the modern understanding of turbulence, a central concept is the existence of cascades of excitations from large to small lengthscales, or vice-versa. This concept was introduce…
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our…