FedLoc: Federated learning framework for data-driven cooperative localization and location data processing
In this overview paper, data-driven learning model-based cooperative localization and
location data processing are considered, in line with the emerging machine learning and big …
location data processing are considered, in line with the emerging machine learning and big …
Bayesian hidden physics models: Uncertainty quantification for discovery of nonlinear partial differential operators from data
S Atkinson - arXiv preprint arXiv:2006.04228, 2020 - arxiv.org
What do data tell us about physics-and what don't they tell us? There has been a surge of
interest in using machine learning models to discover governing physical laws such as …
interest in using machine learning models to discover governing physical laws such as …
Learning while tracking: A practical system based on variational Gaussian process state-space model and smartphone sensory data
We implement a wireless indoor tracking system based on the variational Gaussian process
state-space model (GPSSM) with smartphone-collected WiFi received signal strength and …
state-space model (GPSSM) with smartphone-collected WiFi received signal strength and …
Learning nonlinear state space models with hamiltonian sequential monte carlo sampler
D Xu - arXiv preprint arXiv:1901.00862, 2019 - arxiv.org
State space models (SSM) have been widely applied for the analysis and visualization of
large sequential datasets. Sequential Monte Carlo (SMC) is a very popular particle-based …
large sequential datasets. Sequential Monte Carlo (SMC) is a very popular particle-based …
Learning While Navigating: A Practical System Based on Variational Gaussian Process State-Space Model and Smartphone Sensory Data
We implement a wireless indoor navigation system based on the variational Gaussian
process state-space model (GPSSM) with smartphone-collected WiFi received signal …
process state-space model (GPSSM) with smartphone-collected WiFi received signal …
[引用][C] Using Deep Exponential Families as Generative Models in Marketing Data Fusion
S Postmes - 2019 - Erasmus School of Economics