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Found 10 papers, 1 papers with code
A Unified Kernel for Neural Network Learning
Two predominant approaches have emerged: the Neural Network Gaussian Process (NNGP) and the Neural Tangent Kernel (NTK).
On the Approximation and Complexity of Deep Neural Networks to Invariant Functions
Recent years have witnessed a hot wave of deep neural networks in various domains; however, it is not yet well understood theoretically.
On the Intrinsic Structures of Spiking Neural Networks
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.
Theoretical Exploration of Flexible Transmitter Model
Neural network models generally involve two important components, i. e., network architecture and neuron model.
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions
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.
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values
Multivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values.
Neural Network Gaussian Processes by Increasing Depth
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.
Towards Understanding Theoretical Advantages of Complex-Reaction Networks
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.
Flexible Transmitter Network
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.
Bifurcation Spiking Neural Network
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.
