This seminar aims to introduce cutting-edge research of Data Science, especially, the application of Data Science in scientific problems and the "science" part of Data Science, such as understanding deep learning. Welcome to contact Zhiqin Xu (xuzhiqin@sjtu.edu.cn).
Neural Tangent Kernel: Convergence and Generalization of DNNs
Arthur Jacot,Ecole polytechnique fédérale de Lausanne (EPFL)
2020.05.28, 20:30-21:30

Approximating Quantum Many-Body Wave Functions using Artificial Neural Networks
Zi Cai, School of Physics and Astronomy, Shanghai Jiao Tong University
2020.05.21, 10:30-11:30

Second-Order Type Stochastic Optimization Algorithms for Machine Learning
Zaiwen Wen, Peking University
2020.05.15, 10:30-11:30

Neural Fokker-Planck Equation
Wuchen Li, UCLA Department of Mathematics
2020.05.05, 10:30-11:30

Partial Differential Equation Principled Trustworthy Deep Learning
Bao Wang, University of California, Los Angeles
2020.04.23, 10:30-11:30

The Tradeoffs and Layered Architecture in Brain
Quanying Liu, Southern University of Science and Technology
2020.04.08, 14:00-15:00

Finite Elements and Deep Neural Networks
Juncai He, Penn State University
2020.03.27, 10:00-11:00

A-Priori Estimates of Population Risks for Neural Networks Models
Chao Ma, Princeton University
2020.03.19, 10:00-11:00

Structure Exploration for 3D Reconstruction
Shenghua Gao, ShanghaiTech University
2019.12.18, 12:20-13:50

On the Understanding of Vulnerability of Deep Learning and Beyond
Yisen Wang, Department of Computer Science and Engineering, Shanghai Jiao Tong University
2019.12.11, 15:00-16:00

"Kernel Mode Decomposition and Programmable/Interpretable Regression Networks" by Owhadi Et. Al.
Lei Zhang, Institute of Natural Sciences, Shanghai Jiao Tong University
2019.12.04, 12:20-13:50

Physics-Informed Neural Networks for Solving Forward and Inverse Stochastic Problems
Ling Guo, Shanghai Normal University
2019.11.27, 12:20-13:50