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Screenshot-to-Code

Generating Code from a Graphical User Interface Screenshot

  1. Mouchere, H.; Viard-Gaudin, C.; Kim, D. H.; Kim, J. H.; and Garain, U. 2012. Icfhr 2012 competition on recognition of on-line mathematical expressions (crohme 2012). In Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on, 811–816. IEEE.
  2. Mouchere, H.; Viard-Gaudin, C.; Zanibbi, R.; Garain, U.; Kim, D. H.; and Kim, J. H. 2013. Icdar 2013 crohme: Third international competition on recognition of online handwritten mathematical expressions. In 2013 12th International Conference on Document Analysis and Recognition, 1428–1432. IEEE.
  3. Lin, X.; Gao, L.; Tang, Z.; Lin, X.; and Hu, X. 2012. Performance evaluation of mathematical formula identification. In Document Analysis Systems (DAS), 2012 10th IAPR International Workshop on, 287–291. IEEE.
  4. Nguyen T A, Csallner C. Reverse engineering mobile application user interfaces with remaui (t)[C]//Automated Software Engineering (ASE), 2015 30th IEEE/ACM International Conference on. IEEE, 2015: 248-259.
  5. Deng Y, Kanervisto A, Rush A M. What you get is what you see: A visual markup decompiler[J]. arXiv preprint arXiv:1609.04938, 2016, 10: 32-37.
  6. Beltramelli T. pix2code: Generating code from a graphical user interface screenshot[C]//Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. ACM, 2018: 3.
  7. 三年后，人工智能将彻底改变前端开发？
  8. Emil Wallner, Turning Design Mockups into Code with Deep Learning
  9. Ashwin Kumae, Automated front-end development using deep learning
  10. Airbnb, Sketching Interfaces Generating code from low fidelity wireframes
  11. Florian van Schreven, Code2Pix: Deep Learning Compiler for Graphical User Interfaces
  12. Zhu Z, Xue Z, Yuan Z. Automatic Graphics Program Generation using Attention-Based Hierarchical Decoder[J]. arXiv preprint arXiv:1810.11536, 2018.
  13. Ellis K, Ritchie D, Solar-Lezama A, et al. Learning to infer graphics programs from hand-drawn images[C]//Advances in Neural Information Processing Systems. 2018: 6060-6069.
  14. 代码都智能自动生成了，还要程序员干嘛？
  15. Microsotf, Sketch2Code
  16. uizard - Hand-drawn wireframe to Sketch
  17. uizard - Teaching Machines to Understand User Interfaces
  18. GitHub-fjbriones/pix2code2
    • An attempt to improve pix2code through pretrained autoencoders

Generating Code from Computer program

  1. Graves A, Wayne G, Danihelka I. Neural turing machines[J]. arXiv preprint arXiv:1410.5401, 2014.
  2. Karpathy：利用循环神经网络自动生成代码，早在 2015 年，当时是斯坦福大学计算机科学博士生的 Andrej Karpathy 就使用循环神经网络来生成代码。他使用了一个 Linux 存储库（所有的源文件和 headers files），将它合并成一个巨大的文档（超过 400MB 的代码），并用这些代码来训练 RNN。
  3. Neelakantan, Arvind, Quoc V. Le, and Ilya Sutskever. “Neural prorammer: Inducing latent programs with gradient descent.” arXiv preprint arXiv:1511.04834 (2015).
  4. Riedel S, Bosnjak M, Rocktäschel T. Programming with a differentiable forth interpreter[J]. CoRR, abs/1605.06640, 2016.
  5. Gu X, Zhang H, Zhang D, et al. Deep API learning[C]//Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, 2016: 631-642.
  6. Parisotto E, Mohamed A, Singh R, et al. Neuro-symbolic program synthesis[J]. arXiv preprint arXiv:1611.01855, 2016.
  7. Balog M, Gaunt A L, Brockschmidt M, et al. Deepcoder: Learning to write programs[J]. arXiv preprint arXiv:1611.01989, 2016.
  8. Nachum O, Norouzi M, Schuurmans D. Improving policy gradient by exploring under-appreciated rewards[J]. arXiv preprint arXiv:1611.09321, 2016.
  9. Yang G. Lie access neural turing machine[J]. arXiv preprint arXiv:1602.08671, 2016.
  10. Bello I, Pham H, Le Q V, et al. Neural combinatorial optimization with reinforcement learning[J]. arXiv preprint arXiv:1611.09940, 2016.
  11. Gupta R, Pal S, Kanade A, et al. Deepfix: Fixing common c language errors by deep learning[C]//Thirty-First AAAI Conference on Artificial Intelligence. 2017.
  12. Devlin J, Uesato J, Bhupatiraju S, et al. Robustfill: Neural program learning under noisy I/O[J]. arXiv preprint arXiv:1703.07469, 2017.
  13. Yin P, Neubig G. A syntactic neural model for general-purpose code generation[J]. arXiv preprint arXiv:1704.01696, 2017.
    • 用于将自然语言解析为可执行代码的句法神经模型。
    • GitHub-Code | PDF
  14. Gu X, Zhang H, Zhang D, et al. DeepAM: Migrate APIs with multi-modal sequence to sequence learning[J]. arXiv preprint arXiv:1704.07734, 2017.
  15. Becker K, Gottschlich J. AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms[J]. arXiv preprint arXiv:1709.05703, 2017.
  16. AutoML全称是Automated Machine Learning，是2014年以来机器学习和深度学习领域最炙手可热的领域之一。2017年5月，谷歌在I/O大会上推出基于TensorFlow 框架下的AutoML，它可以通过神经网络来设计神经网络，节省人工设计的时间，其对专业知识要求较低，让非机器学习专家也能轻松上手。
  17. Cai J, Shin R, Song D. Making neural programming architectures generalize via recursion[J]. arXiv preprint arXiv:1704.06611, 2017.
  18. Abolafia D A, Norouzi M, Shen J, et al. Neural program synthesis with priority queue training[J]. arXiv preprint arXiv:1801.03526, 2018.
  19. Kant N. Recent Advances in Neural Program Synthesis[J]. arXiv preprint arXiv:1802.02353, 2018.
  20. Deep Learning for Program Synthesis
  21. PaperWeekly 第43期 | 教机器学习编程
  22. 深度 | 机器的自我进化：走向自主编程的人工智能（附提交ICLR 2017的自动编程论文）
  23. Yin P, Neubig G. A syntactic neural model for general-purpose code generation[J]. arXiv preprint arXiv:1704.01696, 2017.
  24. Zhao R, Bieber D, Swersky K, et al. Neural Networks for Modeling Source Code Edits[J]. 2018.
  25. Sun Z, Zhu Q, Mou L, et al. A Grammar-Based Structural CNN Decoder for Code Generation[J]. arXiv preprint arXiv:1811.06837, 2018.
  26. 杀手级AI补代码工具问世，支持23种语言及5种主流编辑器，程序员沸腾了

DSL

  1. Fowler M. Domain-specific languages[M]. Pearson Education, 2010.
  2. Karsai G, Krahn H, Pinkernell C, et al. Design guidelines for domain specific languages[J]. arXiv preprint arXiv:1409.2378, 2014.

开阔眼界

  1. LeCun 说的可微分编程是什么？