Complexity Measures for Neural Networks with General Activation Functions Using Path-based Norms. - Zhong Li 004; Chao Ma 012; Lei Wu (CoRR, 2020)

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. - Zhong Li 004; Jiequn Han; Weinan E; Qianxiao Li (ICLR, 2021)

Approximation Theory of Convolutional Architectures for Time Series Modelling. - Haotian Jiang; Zhong Li 004; Qianxiao Li (ICML, 2021)

On the approximation properties of recurrent encoder-decoder architectures. - Zhong Li 004; Haotian Jiang; Qianxiao Li (ICLR, 2022)

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. - Zhong Li 004; Jiequn Han; Weinan E; Qianxiao Li (J. Mach. Learn. Res., 2022)

On the Generalization Properties of Diffusion Models. - Puheng Li; Zhong Li 004; Huishuai Zhang; Jiang Bian 002 (NeurIPS, 2023)

AMP: A unified framework boosting low resource automatic speech recognition. - Zeping Min; Qian Ge; Zhong Li 004; Weinan E (CoRR, 2023)

A Brief Survey on the Approximation Theory for Sequence Modelling. - Haotian Jiang; Qianxiao Li; Zhong Li 004; Shida Wang (CoRR, 2023)

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks. - Shida Wang; Zhong Li 004; Qianxiao Li (ICLR, 2024)