4 citations · 10 across the 7 of their papers we have counts for
6 papers
Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective
Zhuotong Chen, Qianxiao Li, Zheng Zhang
The performance of state-of-the-art machine learning models often deteriorates when testing on demographics that are under-represented in the training dataset. This problem has pre…
Interpolation, Approximation and Controllability of Deep Neural Networks
Jingpu Cheng, Qianxiao Li, Ting Lin +1
We investigate the expressive power of deep residual neural networks idealized as continuous dynamical systems through control theory. Specifically, we consider two properties that…
Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks
Haotian Jiang, Qianxiao Li
We present a theoretical analysis of the approximation properties of convolutional architectures when applied to the modeling of temporal sequences. Specifically, we prove an appro…
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems
Qianxiao Li, Ting Lin, Zuowei Shen
We study the approximation of functions which are invariant with respect to certain permutations of the input indices using flow maps of dynamical systems. Such invariant functions…
What Information is Necessary and Sufficient to Predict Materials Properties using Machine Learning?
Siyu Isaac Parker Tian, Aron Walsh, Zekun Ren +2
Conventional wisdom of materials modelling stipulates that both chemical composition and crystal structure are integral in the prediction of physical properties. However, recent de…
Self-Healing Robust Neural Networks via Closed-Loop Control
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Despite the wide applications of neural networks, there have been increasing concerns about their vulnerability issue. While numerous attack and defense techniques have been develo…