no code implementations • 26 Mar 2024 • Shao-Qun Zhang, Zong-Yi Chen, Yong-Ming Tian, Xun Lu
Two predominant approaches have emerged: the Neural Network Gaussian Process (NNGP) and the Neural Tangent Kernel (NTK).
no code implementations • 27 Oct 2022 • Gao Zhang, Jin-Hui Wu, Shao-Qun Zhang
Recent years have witnessed a hot wave of deep neural networks in various domains; however, it is not yet well understood theoretically.
no code implementations • 21 Jun 2022 • Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou
Initially, we unveil two pivotal components of intrinsic structures: the integration operation and firing-reset mechanism, by elucidating their influence on the expressivity of SNNs.
no code implementations • 11 Nov 2021 • Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou
Neural network models generally involve two important components, i. e., network architecture and neuron model.
no code implementations • 8 Nov 2021 • Shao-Qun Zhang, Zhi-Hua Zhou
Mimicking and learning the long-term memory of efficient markets is a fundamental problem in the interaction between machine learning and financial economics to sequential data.
no code implementations • 30 Sep 2021 • Zhao-Yu Zhang, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou
Multivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values.
1 code implementation • 29 Aug 2021 • Shao-Qun Zhang, Fei Wang, Feng-Lei Fan
Inspired by a width-depth symmetry consideration, we use a shortcut network to show that increasing the depth of a neural network can also give rise to a Gaussian process, which is a valuable addition to the existing theory and contributes to revealing the true picture of deep learning.
no code implementations • 15 Aug 2021 • Shao-Qun Zhang, Wei Gao, Zhi-Hua Zhou
Complex-valued neural networks have attracted increasing attention in recent years, while it remains open on the advantages of complex-valued neural networks in comparison with real-valued networks.
no code implementations • 8 Apr 2020 • Shao-Qun Zhang, Zhi-Hua Zhou
To exhibit its power and potential, we present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture taking the FT model as the basic building block.
no code implementations • 18 Sep 2019 • Shao-Qun Zhang, Zhao-Yu Zhang, Zhi-Hua Zhou
Inspired by this insight, by enabling the spike generation function to have adaptable eigenvalues rather than parametric control rates, we develop the Bifurcation Spiking Neural Network (BSNN), which has an adaptive firing rate and is insensitive to the setting of control rates.