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Synthesizing user-intended programs from a small number of input-output examples is a challenging problem with several important applications like spreadsheet manipulation, data wrangling and code refactoring.
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Richard J Waldinger and Richard CT Lee · 1969
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Toward automatic program synthesis
Zohar Manna and Richard J. Waldinger · 1971
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Branch and bound algorithms – principles and examples
Jens Clausen · 1999
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SPIRAL: Code generation for DSP transforms
Markus Puschel, José MF Moura, Jeremy R Johnson, David Padua, Manuela M Veloso, Bryan W Singer, Jianxin Xiong, Franz Franchetti, Aca Gacic, Yevgen Voronenko, et al · 2005
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Automating string processing in spreadsheets using input-output examples
Sumit Gulwani · 2011
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Rajeev Alur, Rastislav Bodík, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, and Abhishek Udupa · 2013
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Growing solver-aided languages with Rosette
Emina Torlak and Rastislav Bodik · 2013
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TRANSIT: Specifying protocols with concolic snippets
Abhishek Udupa, Arun Raghavan, Jyotirmoy V. Deshmukh, Sela Mador-Haim, Milo M.K. Martin, and Rajeev Alur · 2013
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Adam: A method for stochastic optimization
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Łukasz Kaiser and Ilya Sutskever · 2015
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Predicting a correct program in programming by example
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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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Programming with a differentiable Forth interpreter
Matko Bosnjak, Tim Rocktäschel, Jason Naradowsky, and Sebastian Riedel · 2017
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Making neural programming architectures generalize via recursion
Jonathon Cai, Richard Shin, and Dawn Song · 2017
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RobustFill: Neural program learning under noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
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Learning to learn programs from examples: Going beyond program structure
Kevin Ellis and Sumit Gulwani · 2017
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TerpreT: A probabilistic programming language for program induction
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Neural programmer-interpreters
Scott Reed and Nando De Freitas · 2016
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CNTK: Microsoft’s open-source deep-learning toolkit
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Programming by examples: Pl meets ml
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Program synthesis
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Learning syntactic program transformations from examples
Reudismam Rolim, Gustavo Soares, Loris D’Antoni, Oleksandr Polozov, Sumit Gulwani, Rohit Gheyi, Ryo Suzuki, and Björn Hartmann · 2017
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