research
∙
05/23/2023
Subsampling Error in Stochastic Gradient Langevin Diffusions
The Stochastic Gradient Langevin Dynamics (SGLD) are popularly used to a...
share
research
∙
01/27/2023
A fast computational framework for the linear bond-based peridynamic model
Peridynamic (PD) theory is significant and promising in engineering and ...
research
∙
09/08/2022
Losing momentum in continuous-time stochastic optimisation
The training of deep neural networks and other modern machine learning m...
research
∙
12/07/2021
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Optimization problems with continuous data appear in, e.g., robust machi...
research
∙
10/15/2020
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things
Nowadays, Deep Learning as a service can be deployed in Internet of Thin...
research
∙
11/01/2019
rIoT: Enabling Seamless Context-AwareAutomation in the Internet of Things
Advances in mobile computing capabilities and an increasing number of In...
research
∙
06/19/2019
Central limit theorem for a partially observed interacting system of Hawkes processes
We observe the actions of a K sub-sample of N individuals up to time t f...
research
∙
02/19/2019