Search Results for author: David Bieber

Found 13 papers, 8 papers with code

A Library for Representing Python Programs as Graphs for Machine Learning

1 code implementation15 Aug 2022 David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent Hellendoorn, Daniel Johnson, Daniel Tarlow

Graph representations of programs are commonly a central element of machine learning for code research.

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions

1 code implementation7 Mar 2022 David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow

This presents an interesting machine learning challenge: can we predict runtime errors in a "static" setting, where program execution is not possible?

BIG-bench Machine Learning Inductive Bias +1

Show Your Work: Scratchpads for Intermediate Computation with Language Models

no code implementations30 Nov 2021 Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena

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.

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

1 code implementation NeurIPS 2020 David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

More practically, we evaluate these models on the task of learning to execute partial programs, as might arise if using the model as a heuristic function in program synthesis.

Code Completion Learning to Execute +2

Global Relational Models of Source Code

1 code implementation ICLR 2020 Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber

By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation.

Inductive Bias Variable misuse

TF-Coder: Program Synthesis for Tensor Manipulations

2 code implementations NeurIPS Workshop CAP 2020 Kensen Shi, David Bieber, Rishabh Singh

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models.

Enumerative Search

Incremental Sampling Without Replacement for Sequence Models

1 code implementation ICML 2020 Kensen Shi, David Bieber, Charles Sutton

Sampling is a fundamental technique, and sampling without replacement is often desirable when duplicate samples are not beneficial.

Combinatorial Optimization Program Synthesis

Neural Networks for Modeling Source Code Edits

no code implementations4 Apr 2019 Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow

In this work, we instead treat source code as a dynamic object and tackle the problem of modeling the edits that software developers make to source code files.


Neural Program Repair by Jointly Learning to Localize and Repair

2 code implementations ICLR 2019 Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh

We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs.

Variable misuse

PixColor: Pixel Recursive Colorization

no code implementations19 May 2017 Sergio Guadarrama, Ryan Dahl, David Bieber, Mohammad Norouzi, Jonathon Shlens, Kevin Murphy

Then, given the generated low-resolution color image and the original grayscale image as inputs, we train a second CNN to generate a high-resolution colorization of an image.

Colorization Test

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