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Improvements in the performance of computing systems, driven by Moore's Law, have transformed society.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Systematic editing: generating program transformations from an example
Meng, N., Kim, M., and McKinley, K. S · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Stochastic superoptimization
Schkufza, E., Sharma, R., and Aiken, A · 2013
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deltableu: A discriminative metric for generation tasks with intrinsically diverse targets
Galley, M., Brockett, C., Sordoni, A., Ji, Y., Auli, M., Quirk, C., Mitchell, M., Gao, J., and Dolan, B · 2015
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Deepcoder: Learning to write programs
Balog, M., Gaunt, A. L., Brockschmidt, M., Nowozin, S., and Tarlow, D · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
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Neuro-symbolic program synthesis
Parisotto, E., Mohamed, A.-r., Singh, R., Li, L., Zhou, D., and Kohli, P · 2016
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Neural discrete representation learning
Van Den Oord, A., Vinyals, O., et al · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
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Leveraging grammar and reinforcement learning for neural program synthesis
Bunel, R., Hausknecht, M., Devlin, J., Singh, R., and Kohli, P · 2018
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
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Neo: A learned query optimizer
Marcus, R., Negi, P., Mao, H., Zhang, C., Alizadeh, M., Kraska, T., Papaemmanouil, O., and Tatbul, N · 2019
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Learning to infer program sketches
Nye, M., Hewitt, L., Tenenbaum, J., and Solar-Lezama, A · 2019
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There’s plenty of room at the top: What will drive computer performance after moore’s law?
Leiserson, C. E., Thompson, N. C., Emer, J. S., Kuszmaul, B. C., Lampson, B. W., Sanchez, D., and Schardl, T. B · 2020
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Learning to represent programs with property signatures
Odena, A. and Sutton, C · 2020
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Kalyan, A., Mohta, A., Polozov, O., Batra, D., Jain, P., and Gulwani, S · 2018
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Learning to optimize join queries with deep reinforcement learning
Krishnan, S., Yang, Z., Goldberg, K., Hellerstein, J., and Stoica, I · 2018
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Accelerating search-based program synthesis using learned probabilistic models
Lee, W., Heo, K., Alur, R., and Naik, M · 2018
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Neural networks for modeling source code edits.(2018)
Zhao, R., Bieber, D., Swersky, K., and Tarlow, D · 2018
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al
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Plur: A unifying, graph-based view of program learning, understanding, and repair
Chen, Z., Hellendoorn, V. J., Lamblin, P., Maniatis, P., Manzagol, P.-A., Tarlow, D., and Moitra, S
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Code-jam competition archive
Code-Jam, G
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Graph-based, self-supervised program repair from diagnostic feedback
Yasunaga, M. and Liang, P · 2020
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Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
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Carbon emissions and large neural network training
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., and Dean, J · 2021
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2022
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