614 citations · 1.8k across the 62 of their papers we have counts for
6 papers · 1 filter
Efficient and Reliable Overlay Networks for Decentralized Federated Learning
Yifan Hua, Kevin Miller, Andrea L. Bertozzi +2
We propose near-optimal overlay networks based on -regular expander graphs to accelerate decentralized federated learning (DFL) and improve its generalization. In DFL a massive…
An Analysis of COVID-19 Knowledge Graph Construction and Applications
Dominic Flocco, Bryce Palmer-Toy, Ruixiao Wang +5
The construction and application of knowledge graphs have seen a rapid increase across many disciplines in recent years. Additionally, the problem of uncovering relationships betwe…
Heavy Ball Neural Ordinary Differential Equations
Hedi Xia, Vai Suliafu, Hangjie Ji +4
We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the continuous limit of the classical momentum accelerated gradient descent, to improve neural OD…
Model-Change Active Learning in Graph-Based Semi-Supervised Learning
Kevin Miller, Andrea L. Bertozzi
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to…
A Multilayer Network Model of the Coevolution of the Spread of a Disease and Competing Opinions
Kaiyan Peng, Zheng Lu, Vanessa Lin +5
During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phen…
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application
Hangjie Ji, Kyle Lafata, Yvonne Mowery +4
This paper develops a method of biologically guided deep learning for post-radiation FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose informatio…