Qianxiao Li
Postgraduate:
Main Appointment:
Joint Appointments:
Research Fields:
Research Areas:
Research Fields:
- STEMM – Science, Technology, Engineering, Mathematics, Medical Sciences
Research Keywords:
- Machine Learning
- Deep Learning
- Numerical Analysis
- Optimization
- Control
Current Appointments:
Brief Description of Research:
My research is on theoretical machine learning and its connections with numerical analysis, dynamical systems, and optimization/optimal control. I am also interested in developing novel applications of data-driven methods for scientific discovery.
Total Number of Publications:
Five Representative Publications:
Li, Qianxiao, and Shuji Hao. “An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks.” In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
Li, Qianxiao, Cheng Tai, and Weinan E. “Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms.” In Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.
Li, Qianxiao, Long Chen, Cheng Tai, and Weinan E. “Maximum Principle Based Algorithms for Deep Learning.” The Journal of Machine Learning Research 18, no. 1 (2018): 5998–6026.
Li, Qianxiao, Cheng Tai, and Weinan E. “Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations.” Journal of Machine Learning Research 20, no. 40 (2019): 1–47.
Cai, Yongqiang, Qianxiao Li, and Zuowei Shen. “A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent.” In International Conference on Machine Learning (ICML), 882–890, 2019.
My Research Videos:
Top 5 Publications:
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Journals Published:
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Appointments
Education
Research Areas
- Machine Learning
- Deep Learning
- Numerical Analysis
- Optimization
- Control
Research Description
My research is on theoretical machine learning and its connections with numerical analysis, dynamical systems, and optimization/optimal control. I am also interested in developing novel applications of data-driven methods for scientific discovery.
Research Videos
Selected Publications
Li, Qianxiao, and Shuji Hao. “An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks.” In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
Li, Qianxiao, Cheng Tai, and Weinan E. “Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms.” In Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.
Li, Qianxiao, Long Chen, Cheng Tai, and Weinan E. “Maximum Principle Based Algorithms for Deep Learning.” The Journal of Machine Learning Research 18, no. 1 (2018): 5998–6026.
Li, Qianxiao, Cheng Tai, and Weinan E. “Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations.” Journal of Machine Learning Research 20, no. 40 (2019): 1–47.
Cai, Yongqiang, Qianxiao Li, and Zuowei Shen. “A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent.” In International Conference on Machine Learning (ICML), 882–890, 2019.