We perform a careful, thorough, and large scale empirical study of the correspondence
between wide neural networks and kernel methods. By doing so, we resolve a variety of …

The permeability coefficient (k) of soil is one of the most important parameters affecting soil
characteristics such as shear strength or settlement. Thus, determining soil permeability …

The predictions of wide Bayesian neural networks are described by a Gaussian process,
known as the Neural Network Gaussian Process (NNGP). Analytic forms for NNGP kernels …

In order to study the driving state of heavy vehicles, two approaches are employed hereby to
establish digital twin models for analyzing the applicable scopes of the models and …

Classical denoising techniques are efficient to extinguish the Gaussian noise but are unable
to handle the impulse and additive noise. The blurring of edges in denoised data is critical in …

In artificial intelligence, combating overfitting and enhancing model generalization is crucial.
This research explores innovative noise-induced regularization techniques, focusing on …

This paper presents a proposal to combine supervised and semi-supervised training
strategies to obtain a neural network for use in the prescriptive maintenance approach. It is …