no code implementations • 26 Oct 2023 • Andrew Davison, S. Carlyle Morgan, Owen G. Ward
Embedding the nodes of a large network into an Euclidean space is a common objective in modern machine learning, with a variety of tools available.
no code implementations • 17 Dec 2020 • Owen G. Ward, Jing Wu, Tian Zheng, Anna L. Smith, James P. Curley
We compare all models using simulated and real data.
Applications Methodology
1 code implementation • 3 Sep 2020 • Guanhua Fang, Owen G. Ward, Tian Zheng
To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models.
no code implementations • 10 Jul 2020 • Owen G. Ward, Zhen Huang, Andrew Davison, Tian Zheng
Embedding nodes of a large network into a metric (e. g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences.