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Found 13 papers, 8 papers with code
A Library for Representing Python Programs as Graphs for Machine Learning
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
This presents an interesting machine learning challenge: can we predict runtime errors in a "static" setting, where program execution is not possible?
Learning Semantic Representations to Verify Hardware Designs
We evaluate Design2Vec on three real-world hardware designs, including an industrial chip used in commercial data centers.
Show Your Work: Scratchpads for Intermediate Computation with Language Models
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
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.
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Program synthesis is challenging largely because of the difficulty of search in a large space of programs.
Global Relational Models of Source Code
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.
TF-Coder: Program Synthesis for Tensor Manipulations
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.
Incremental Sampling Without Replacement for Sequence Models
Sampling is a fundamental technique, and sampling without replacement is often desirable when duplicate samples are not beneficial.
Neural Networks for Modeling Source Code Edits
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
We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs.
PixColor: Pixel Recursive Colorization
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.
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Colorization
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