Abstract: In this work, we propose a high-order regularization method to solve the ill-conditioned problems in robot localization. Numerical solutions to robot localization problems are often unstable ...

Abstract: Due to the sparse feature enhancement only concentrating on strong scatterers of target of interest, the conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters ...

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental ...

A hands-on tutorial to understand L1 (Lasso) and L2 (Ridge) regularization using Python and Scikit-learn with visual and performance comparison. This repository provides a detailed and practical ...

Here is the PyTorch implementation for paper Enhancing Generalization of Spiking Neural Networks Through Temporal Regularization. Spiking Neural Networks (SNNs) have received widespread attention due ...

This study presents a new and important theoretical account of spatial representational drift in the hippocampus. The evidence supporting the claims is convincing, with a clear and accessible ...

This thesis is about regularization of ultraviolet divergences appearing in the perturbative expansion of quantum field theories (QFTs). We present a general view of the ambiguities that may arise in ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...