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Charles A. Sutton

2 accepted papers

2020

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration

NeurIPS 2020poster

Discrete structures play an important role in applications like program language modeling and software engineering. Current approaches to predicting complex structures typically consider autoregressive models for their tractability, with some sacrifice in flexibility.

2020

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

NeurIPS 2020poster

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 leveraging program structure like control flow graphs, but they are not well-suited to tasks like program execution that…