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Programming is a powerful and ubiquitous problem-solving tool.
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M. Bruch, M. Monperrus, and M. Mezini · 2009
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PPDB: The paraphrase database
J. Ganitkevitch, B. Van Durme, and C. Callison-Burch · 2013
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Adam: A method for stochastic optimization
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The Rust language
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Code completion with statistical language models
V. Raychev, M. Vechev, and E. Yahav · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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DeepCoder: Learning to write programs
M. Balog, A. L. Gaunt, M. Brockschmidt, S. Nowozin, and D. Tarlow · 2016
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Description2Code Dataset, 8 2016
E. Caballero, OpenAI, and I. Sutskever · 2016
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Latent predictor networks for code generation
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RobustFill: Neural program learning under noisy I/O
J. Devlin, J. Uesato, S. Bhupatiraju, R. Singh, A.-r. Mohamed, and P. Kohli · 2017
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Program synthesis
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
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Neural sketch learning for conditional program generation
V. Murali, L. Qi, S. Chaudhuri, and C. Jermaine · 2017
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Attention is all you need
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A syntactic neural model for general-purpose code generation
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JAX: composable transformations of Python+NumPy programs, 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Understanding back-translation at scale
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Hierarchical neural story generation
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NAPS: Natural program synthesis dataset
M. Zavershynskyi, A. Skidanov, and I. Polosukhin · 2018
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The adverse effects of code duplication in machine learning models of code
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The curious case of neural text degeneration
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Achieving verified robustness to symbol substitutions via interval bound propagation
Solving linear algebra by program synthesis
I. Drori and N. Verma · 2021
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How to interpret contest ratings
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Facebook hacker cup
Facebook Hacker Cup · 2021
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Google Code Jam
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Learning to generate code sketches
D. Guo, A. Svyatkovskiy, J. Yin, N. Duan, M. Brockschmidt, and M. Allamanis · 2021
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Measuring coding challenge competence with APPS
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P.-S. Huang, R. Stanforth, J. Welbl, C. Dyer, D. Yogatama, S. Gowal, K. Dvijotham, and P. Kohli · 2019
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Spoc: Search-based pseudocode to code
S. Kulal, P. Pasupat, K. Chandra, M. Lee, O. Padon, A. Aiken, and P. S. Liang · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
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Fast transformer decoding: One write-head is all you need
N. Shazeer · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, et al · 2019
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Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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PyMT5: multi-mode translation of natural language and Python code with transformers
C. B. Clement, D. Drain, J. Timcheck, A. Svyatkovskiy, and N. Sundaresan · 2020
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International collegiate programming contest
ICPC · 2021
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ICPC factsheet
ICPC Factsheet · 2021
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ICPC rules
ICPC Rules · 2021
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International olympiad in informatics
IOI · 2021
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Ten lessons from three generations shaped Google’s TPUv4i
N. P. Jouppi, D. H. Yoon, M. Ashcraft, M. Gottscho, T. B. Jablin, G. Kurian, J. Laudon, S. Li, P. Ma, X. Ma, et al · 2021
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Codeforces: Results of 2020
M. Mirzayanov · 2021
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Neural learning of one-of-many solutions for combinatorial problems in structured output spaces
Y. Nandwani, D. Jindal, Mausam, and P. Singla · 2021
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An empirical cybersecurity evaluation of GitHub Copilot’s code contributions
H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri · 2021
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Project CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks
R. Puri, D. S. Kung, G. Janssen, W. Zhang, G. Domeniconi, V. Zolotov, J. Dolby, J. Chen, M. Choudhury, L. Decker, et al · 2021
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Scaling language models: Methods, analysis & insights from training Gopher
J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, J. Aslanides, S. Henderson, R. Ring, S. Young, et al · 2021
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Solving probability and statistics problems by program synthesis
L. Tang, E. Ke, N. Singh, N. Verma, and I. Drori · 2021
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Learning to synthesize programs as interpretable and generalizable policies
D. Trivedi, J. Zhang, S.-H. Sun, and J. J. Lim · 2021
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Ethical and social risks of harm from language models
L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P. Huang, M. Cheng, M. Glaese, B. Balle, A. Kasirzadeh, Z. Kenton, S. Brown, W. Hawkins, T. Stepleton, C. Biles, A. Birhane, J. Haas, L. Rimell, L. A. Hendricks, W. S. Isaac, S. Legassick, G. Irving, and I. Gabriel · 2021
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Research recitation: A first look at rote learning in GitHub Copilot suggestions
Albert Ziegler · 2022
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GitHub’s automatic coding tool rests on untested legal ground
D. Gershgorn · 2022
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Meeting our match: Buying 100 percent renewable energy
U. Hölzle · 2022
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Falsehoods programmers believe about names
P. McKenzie · 2022
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Falsehoods programmers believe about time
N. Sussman · 2022
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Falsehoods programmers believe about addresses
M. Tandy · 2022
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