11 citations · 12 across the 4 of their papers we have counts for
4 papers
Efficient Model Adaptation for Continual Learning at the Edge
Zachary A. Daniels, Jun Hu, Michael Lomnitz +5
Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed o…
Experimental observation on a low-rank tensor model for eigenvalue problems
Jun Hu, Pengzhan Jin
Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator a…
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels +8
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at th…
Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels
Jun Hu, Yang Xiang
A pseudo independent (PI) model is a probabilistic domain model (PDM) where proper subsets of a set of collectively dependent variables display marginal independence. PI models can…