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We introduce a new type of programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis, and release an open-source dataset of Python Programming Puzzles (P3).
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Finite Groups of Automorphisms: Course Given at the University of Southampton, October-December 1969
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Sumit Gulwani · 2011
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Scikit-learn: Machine learning in Python
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Bootstrap learning via modular concept discovery
Eyal Dechter, Jonathan Malmaud, Ryan P Adams, and Joshua B Tenenbaum · 2013
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A machine learning framework for programming by example
Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W Lampson, and Adam Kalai · 2013
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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FlashMeta: A framework for inductive program synthesis
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ImageNet Large Scale Visual Recognition Challenge
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Deepcoder: Learning to write programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2017
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Program synthesis
Sumit Gulwani, Oleksandr Polozov, Rishabh Singh, et al · 2017
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Mastering the game of go without human knowledge
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Glass-box program synthesis: A machine learning approach
Konstantina Christakopoulou and Adam Tauman Kalai · 2018
Structural language models of code
Uri Alon, Roy Sadaka, Omer Levy, and Eran Yahav · 2020
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Task-oriented dialogue as dataflow synthesis
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Five $1,000 problems (update 2017)
John Horton Conway · 2020
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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NAPS: natural program synthesis dataset
Maksym Zavershynskyi, Alexander Skidanov, and Illia Polosukhin · 2018
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SWAG: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi · 2018
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SyGuS-Comp 2018: Results and analysis
Rajeev Alur, Dana Fisman, Saswat Padhi, Rishabh Singh, and Abhishek Udupa · 2019
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
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Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
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Guide to Competitive Programming: Learning and Improving Algorithms Through Contests
A. Laaksonen · 2020
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Python One-Liners: Write Concise, Eloquent Python Like a Professional
C. Mayer · 2020
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Codebleu: a method for automatic evaluation of code synthesis, 2020
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma · 2020
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IntelliCode Compose: code generation using Transformers
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Program synthesis with large language models, 2021
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Evaluating large language models trained on code, 2021
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Measuring coding challenge competence with APPS
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Dynabench: Rethinking benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, and Adina Williams · 2021
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Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark
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Codexglue: A machine learning benchmark dataset for code understanding and generation, 2021
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu · 2021
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Karel the robot learns python
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Research recitation: A first look at rote learning in github copilot suggestions., June 2021
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