46 citations · 147 across the 10 of their papers we have counts for
11 papers
A Library for Representing Python Programs as Graphs for Machine Learning
David Bieber, Kensen Shi, Petros Maniatis +4
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static…
Language Model Cascades
David Dohan, Winnie Xu, Aitor Lewkowycz +9
Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, fu…
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
David Bieber, Rishab Goel, Daniel Zheng +2
The execution behavior of a program often depends on external resources, such as program inputs or file contents, and so cannot be run in isolation. Nevertheless, software develope…
Show Your Work: Scratchpads for Intermediate Computation with Language Models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari +9
Large pre-trained language models perform remarkably well on tasks that can be done "in one pass", such as generating realistic text or synthesizing computer programs. However, the…
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…
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber +3
Program synthesis is challenging largely because of the difficulty of search in a large space of programs. Human programmers routinely tackle the task of writing complex programs b…