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R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents.

D. Johnson, D. Tarlow, и C. Walder. ICML, том 202 из Proceedings of Machine Learning Research, стр. 15262-15306. PMLR, (2023)

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Structured Denoising Diffusion Models in Discrete State-Spaces.J. Austin, D. Johnson, J. Ho, D. Tarlow, и R. van den Berg. NeurIPS, стр. 17981-17993. (2021)A density estimation perspective on learning from pairwise human preferences.V. Dumoulin, D. Johnson, P. Castro, H. Larochelle, и Y. Dauphin. CoRR, (2023)Learning Graph Structure With A Finite-State Automaton Layer.D. Johnson, H. Larochelle, и D. Tarlow. NeurIPS, (2020)LEG processor for education.M. Waugaman, Z. Davidson, S. Dietrich, D. Johnson, C. Meyer, E. Storm, A. Thaker, и I. Wong. EWME, стр. 1-5. IEEE, (2016)Penzai + Treescope: A Toolkit for Interpreting, Visualizing, and Editing Models As Data.D. Johnson. CoRR, (2024)A Library for Representing Python Programs as Graphs for Machine Learning.D. Bieber, K. Shi, P. Maniatis, C. Sutton, V. Hellendoorn, D. Johnson, и D. Tarlow. CoRR, (2022)Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments.D. Johnson, D. Gorelik, R. Mawhorter, K. Suver, W. Gu, S. Xing, C. Gabriel, и P. Sankhagowit. NeurIPS, стр. 3469-3478. (2018)Learning Generalized Gumbel-max Causal Mechanisms.G. Lorberbom, D. Johnson, C. Maddison, D. Tarlow, и T. Hazan. NeurIPS, стр. 26792-26803. (2021)A density estimation perspective on learning from pairwise human preferences.V. Dumoulin, D. Johnson, P. Castro, H. Larochelle, и Y. Dauphin. Trans. Mach. Learn. Res., (2024)Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming.A. Paszke, D. Johnson, D. Duvenaud, D. Vytiniotis, A. Radul, M. Johnson, J. Ragan-Kelley, и D. Maclaurin. Proc. ACM Program. Lang., 5 (ICFP): 1-29 (2021)

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