Bifurcation Spiking Neural Network. - Shao-Qun Zhang; Zhi-Hua Zhou (CoRR, 2019)

Harmonic Recurrent Process for Time Series Forecasting. - Shao-Qun Zhang; Zhi-Hua Zhou (ECAI, 2020)

LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values. - Zhao-Yu Zhang; Shao-Qun Zhang; Yuan Jiang 001; Zhi-Hua Zhou (ICDM, 2021)

Towards Understanding Theoretical Advantages of Complex-Reaction Networks. - Shao-Qun Zhang; Gao Wei; Zhi-Hua Zhou (CoRR, 2021)

Neural Network Gaussian Processes by Increasing Depth. - Shao-Qun Zhang; Feng-Lei Fan (CoRR, 2021)

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)

ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions. - Shao-Qun Zhang; Zhi-Hua Zhou (CoRR, 2021)

Bifurcation Spiking Neural Network. - Shao-Qun Zhang; Zhao-Yu Zhang; Zhi-Hua Zhou (J. Mach. Learn. Res., 2021)

Flexible Transmitter Network. - Shao-Qun Zhang; Zhi-Hua Zhou (Neural Comput., 2021)

Fault-Tolerant Energy-Efficient LoRaWAN Networking Architecture. - Cheng-En Yu; Chao Wang 052; Shao-Qun Zhang (MECO, 2022)

Theoretically Provable Spiking Neural Networks. - Shao-Qun Zhang; Zhi-Hua Zhou (NeurIPS, 2022)

Structural Stability of Spiking Neural Networks. - Gao Zhang; Shao-Qun Zhang (CoRR, 2022)

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

Towards understanding theoretical advantages of complex-reaction networks. - Shao-Qun Zhang; Wei Gao 008; Zhi-Hua Zhou (Neural Networks, 2022)

On the Consistency Rate of Decision Tree Learning Algorithms. - Qin-Cheng Zheng; Shen-Huan Lyu; Shao-Qun Zhang; Yuan Jiang 001; Zhi-Hua Zhou (AISTATS, 2023)

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)

On Discrete Presheaf Monads. - Gao Zhang; Shao-Qun Zhang (Axioms, 2023)

Lax Extensions of Conical I-Semifilter Monads. - Gao Zhang; Shao-Qun Zhang (Axioms, 2023)

MEPSI: An MDL-Based Ensemble Pruning Approach with Structural Information. - Xiao-Dong Bi; Shao-Qun Zhang; Yuan Jiang 001 (AAAI, 2024)

A Unified Kernel for Neural Network Learning. - Shao-Qun Zhang; Zong-Yi Chen; Yong-Ming Tian; Xun Lu (CoRR, 2024)

Horizon-wise Learning Paradigm Promotes Gene Splicing Identification. - Qi-Jie Li; Qian Sun; Shao-Qun Zhang (CoRR, 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)

Neural Network Gaussian Processes by Increasing Depth. - Shao-Qun Zhang; Fei Wang 001; Feng-Lei Fan (IEEE Trans. Neural Networks Learn. Syst., 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)

Theoretical Investigation on Inductive Bias of Isolation Forest. - Qin-Cheng Zheng; Shao-Qun Zhang; Shen-Huan Lyu; Yuan Jiang 001; Zhi-Hua Zhou (CoRR, 2025)