Can Large Language Models Reason about Program Invariants?
Proceedings of the International Conference on Machine Learning, 2023
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
TF-Coder: Program Synthesis for Tensor Manipulations.
ACM Trans. Program. Lang. Syst., 2022
A Library for Representing Python Programs as Graphs for Machine Learning.
CoRR, 2022
Show Your Work: Scratchpads for Intermediate Computation with Language Models.
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CoRR, 2021
Learning Semantic Representations to Verify Hardware Designs.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration.
Proceedings of the 9th International Conference on Learning Representations, 2021
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration.
CoRR, 2020
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Incremental Sampling Without Replacement for Sequence Models.
Proceedings of the 37th International Conference on Machine Learning, 2020
Global Relational Models of Source Code.
Proceedings of the 8th International Conference on Learning Representations, 2020
Neural Networks for Modeling Source Code Edits.
CoRR, 2019
Neural Program Repair by Jointly Learning to Localize and Repair.
Proceedings of the 7th International Conference on Learning Representations, 2019
PixColor: Pixel Recursive Colorization.
Proceedings of the British Machine Vision Conference 2017, 2017