432 citations · 2.3k across the 29 of their papers we have counts for
6 papers · 1 filter
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…
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…
T2: Temporal Property Verification
Marc Brockschmidt, Byron Cook, Samin Ishtiaq +2
We present the open-source tool T2, the first public release from the TERMINATOR project. T2 has been extended over the past decade to support automatic temporal-logic proving tech…