432 citations · 1.2k across the 38 of their papers we have counts for
5 papers · 1 filter
Learning to Fix Build Errors with Graph2Diff Neural Networks
Daniel Tarlow, Subhodeep Moitra, Andrew Rice +4
Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new…
Fast Training of Sparse Graph Neural Networks on Dense Hardware
Matej Balog, Bart van Merriënboer, Subhodeep Moitra +2
Graph neural networks have become increasingly popular in recent years due to their ability to naturally encode relational input data and their ability to scale to large graphs by…
Learning Execution through Neural Code Fusion
Zhan Shi, Kevin Swersky, Daniel Tarlow +2
As the performance of computer systems stagnates due to the end of Moore's Law, there is a need for new models that can understand and optimize the execution of general purpose cod…
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Guy Lorberbom, Chris J. Maddison, Nicolas Heess +2
Direct optimization is an appealing framework that replaces integration with optimization of a random objective for approximating gradients in models with discrete random variables…
Neural Networks for Modeling Source Code Edits
Rui Zhao, David Bieber, Kevin Swersky +1
Programming languages are emerging as a challenging and interesting domain for machine learning. A core task, which has received significant attention in recent years, is building…