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A Library for Representing Python Programs as Graphs for Machine Learning


Aug 15, 2022
David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent Hellendoorn, Daniel Johnson, Daniel Tarlow

* 21 pages, 14 figures 

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Learning to Improve Code Efficiency


Aug 09, 2022
Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan


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Repository-Level Prompt Generation for Large Language Models of Code


Jun 26, 2022
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow


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


Mar 07, 2022
David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow

* 20 pages, 7 figures 

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Learning Generalized Gumbel-max Causal Mechanisms


Nov 11, 2021
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan

* Accepted to NeurIPS 2021 (Spotlight) 

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Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models


Jul 16, 2021
Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow

* Accepted at the ICML 2021 Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (poster) 

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Structured Denoising Diffusion Models in Discrete State-Spaces


Jul 13, 2021
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

* 10 pages plus references and appendices. First two authors contributed equally 

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Learning to Combine Per-Example Solutions for Neural Program Synthesis


Jun 14, 2021
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow


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Learning to Extend Program Graphs to Work-in-Progress Code


May 28, 2021
Xuechen Li, Chris J. Maddison, Daniel Tarlow


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Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks


Oct 23, 2020
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

* Accepted at NeurIPS 2020 

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