103 citations · 266 across the 6 of their papers we have counts for
6 papers
Neural Program Repair by Jointly Learning to Localize and Repair
Marko Vasic, Aditya Kanade, Petros Maniatis +2
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural network…
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…
SyGuS-Comp 2016: Results and Analysis
Rajeev Alur, Dana Fisman, Rishabh Singh +1
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula in a background the…
Neuro-Symbolic Program Synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh +3
Recent years have seen the proposal of a number of neural architectures for the problem of Program Induction. Given a set of input-output examples, these architectures are able to…
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…
Learning Semantic String Transformations from Examples
Rishabh Singh, Sumit Gulwani
We address the problem of performing semantic transformations on strings, which may represent a variety of data types (or their combination) such as a column in a relational table,…