all AI news
Mixed-Output Gaussian Process Latent Variable Models
Feb. 15, 2024, 5:43 a.m. | James Odgers, Chrysoula Kappatou, Ruth Misener, Sarah Filippi
cs.LG updates on arXiv.org arxiv.org
Abstract: This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Process Latent Variable Models (GPLVMs) to incorporate the case where each data point comprises the weighted sum of a known number of pure component signals, observed across several input locations. Our framework allows the use of a range of priors for the weights of each observation. This flexibility enables …
abstract arxiv bayesian case cs.lg data key mixed non-parametric parametric process signal stat.ml type variables work
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Business Data Analyst
@ Alstom | Johannesburg, GT, ZA