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Models and examples built with TensorFlow
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.
Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new tool.
We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.
Neural models optimized for tree-based problems are of great value in tasks like SQL query extraction and program synthesis.
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.
It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.
Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design.
Second, we present a self-supervised learning paradigm for program repair that leverages unlabeled programs available online to create a large amount of extra program repair examples, which we use to pre-train our models.
Ranked #1 on Program Repair on DeepFix