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个人简介
My current research focuses on (1) integrating deep learning with symbolic modules; (2) making reinforcement learning more efficient; (3) applications in real-world natural language understanding and program synthesis tasks. For example, my recent works designed a hybrid neural network model (Neural Symbolic Machines) and proposed novel policy gradient methods for efficient training. It is the first end-to-end neural network model that achieves new stateof-the-art on the most challenging semantic parsing benchmarks. The model learns from weak supervision to answer compositional questions by generating programs to query and process data from knowledge graph or database tables.
研究兴趣
论文共 26 篇作者统计合作学者相似作者
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Ryan Gillard, Stephen Jonany, Yingjie Miao, Michael Munn, Connal de Souza, Jonathan Dungay,Chen Liang, David R. So,Quoc V. Le,Esteban Real
arxiv(2023)
引用0浏览0引用
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Xiangning Chen,Chen Liang, Da Huang,Esteban Real, Kaiyuan Wang, Yao Liu,Hieu Pham, Xuanyi Dong, Thang Luong,Cho-Jui Hsieh,Yifeng Lu,Quoc V. Le
arxiv(2023)
引用16浏览0引用
16
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Sheng Li, Garrett Andersen, Tao Chen,Liqun Cheng, Julian Grady, Da Huang,Quoc V. Le,Andrew Li, Xin Li, Yang Li,Chen Liang,Yifeng Lu,
ASPLOS 2023: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3pp.343-358, (2023)
David Patterson,Joseph Gonzalez,Urs Hölzle,Quoc Hung Le,Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier,Jeffrey Dean
semanticscholar(2022)
David Patterson,Joseph Gonzalez,Quoc Le,Chen Liang, Lluis-Miquel Munguia,Daniel Rothchild, David So, Maud Texier,Jeff Dean
arxiv(2021)
引用178浏览0EI引用
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