Towards Theoretical Understanding of Flexible Transmitter Networks via Approximation and Local Minima. - Jin-Hui Wu; Shao-Qun Zhang; Yuan Jiang 001; Zhi-Hua Zhou (CoRR, 2021)

On the Approximation and Complexity of Deep Neural Networks to Invariant Functions. - Gao Zhang; Jin-Hui Wu; Shao-Qun Zhang (CoRR, 2022)

Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent. - Jin-Hui Wu; Shao-Qun Zhang; Yuan Jiang 001; Zhi-Hua Zhou (NeurIPS, 2023)

The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest. - Shen-Huan Lyu; Jin-Hui Wu; Qin-Cheng Zheng; Baoliu Ye (ECAI, 2024)

On the Intrinsic Structures of Spiking Neural Networks. - Shao-Qun Zhang; Jia-Yi Chen; Jin-Hui Wu; Gao Zhang; Huan Xiong; Bin Gu 001; Zhi-Hua Zhou (J. Mach. Learn. Res., 2024)

Theoretical Exploration of Flexible Transmitter Model. - Jin-Hui Wu; Shao-Qun Zhang; Yuan Jiang 001; Zhi-Hua Zhou (IEEE Trans. Neural Networks Learn. Syst., 2024)