Author: Asher Collins

Authors: Hamied Nabizada、Tom Jeleniewski、Felix Gehlhoff、Alexander Fay Paper: https://arxiv.org/abs/2408.08145 Introduction The rapid advancement of technology has led to increasingly complex production systems and products. Model-Based Systems Engineering (MBSE) has emerged as a powerful approach to address these challenges by providing a structured methodology for modeling, analyzing, and managing complex systems. MBSE promotes the use of models to provide detailed and consistent descriptions of production systems, facilitating efficient collaboration among various disciplines involved in the development process. However, MBSE methods often require numerous manual steps, such as assigning individual process steps to potential technical resources, which demands a high level of expert…

Authors: Jian Xu、Shian Du、Junmei Yang、Qianli Ma、Delu Zeng Paper: https://arxiv.org/abs/2408.06710 Introduction Gaussian Process Latent Variable Models (GPLVMs) have gained popularity for unsupervised learning tasks such as dimensionality reduction and missing data recovery. These models are flexible and non-linear, making them suitable for complex data structures. However, traditional methods face challenges in high-dimensional spaces. This paper introduces a novel approach using Stochastic Gradient Annealed Importance Sampling (SG-AIS) to address these issues. Background GPLVM Variational Inference GPLVMs map high-dimensional data to a lower-dimensional latent space using Gaussian Processes (GPs). The Bayesian version of GPLVMs uses sparse representations to reduce model complexity. The evidence…