432 citations · 1.2k across the 38 of their papers we have counts for
5 papers · 1 filter
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber, Charles Sutton, Hugo Larochelle +1
Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from l…
Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs
Kian Ahrabian, Daniel Tarlow, Hehuimin Cheng +1
We present a multi-relational temporal Knowledge Graph based on the daily interactions between artifacts in GitHub, one of the largest social coding platforms. Such representation…
Learning Graph Structure With A Finite-State Automaton Layer
Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relation…
Gradient Estimation with Stochastic Softmax Tricks
Max B. Paulus, Dami Choi, Daniel Tarlow +2
The Gumbel-Max trick is the basis of many relaxed gradient estimators. These estimators are easy to implement and low variance, but the goal of scaling them comprehensively to larg…
On-the-Fly Adaptation of Source Code Models using Meta-Learning
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
The ability to adapt to unseen, local contexts is an important challenge that successful models of source code must overcome. One of the most popular approaches for the adaptation…