| SymPy: symbolic computing in Python A Meurer, CP Smith, M Paprocki, O Čertík, SB Kirpichev, M Rocklin, ... PeerJ Computer Science 3, e103, 2017 | 1024 | 2017 |
| Spoc: Search-based pseudocode to code S Kulal, P Pasupat, K Chandra, M Lee, O Padon, A Aiken, PS Liang Advances in Neural Information Processing Systems 32, 2019 | 66 | 2019 |
| Contract-based resource verification for higher-order functions with memoization R Madhavan, S Kulal, V Kuncak Acm Sigplan Notices 52 (1), 330-343, 2017 | 37 | 2017 |
| What’s hard about Boolean functional synthesis? S Akshay, S Chakraborty, S Goel, S Kulal, S Shah Computer Aided Verification: 30th International Conference, CAV 2018, Held …, 2018 | 20 | 2018 |
| Hierarchical motion understanding via motion programs S Kulal, J Mao, A Aiken, J Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 6 | 2021 |
| Boolean functional synthesis: hardness and practical algorithms S Akshay, S Chakraborty, S Goel, S Kulal, S Shah Formal Methods in System Design 57, 53-86, 2021 | 4 | 2021 |
| Space Leaks Exploration in Haskell S Kulal, R Ganvir, S Sudhakaran Indian Institute of Technology Bombay Mumbai, 0 | 1 | |
| Unsupervised Learning of Shape Programs with Repeatable Implicit Parts B Deng, S Kulal, Z Dong, C Deng, Y Tian, J Wu Advances in Neural Information Processing Systems, 2022 | | 2022 |
| Programmatic Concept Learning for Human Motion Description and Synthesis S Kulal, J Mao, A Aiken, J Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | | 2022 |
| Scalable Synthesis with Symbolic Syntax Graphs R Shah, S Kulal, R Bodik | | |
Alex AikenProfessor of Computer Science, Stanford UniversityZweryfikowany adres z cs.stanford.edu
