most citedOn Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction

Ubaid Azam, Imran Razzak, Shelly Vishwakarma +3

Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications. While transformer-based deep neural networks (DNNs) like BERT have demonstra…

cs.LG2024

MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization

Jingwei Zuo, George Arvanitakis, Mthandazo Ndhlovu +1

Human activity recognition (HAR) is a well-established field, significantly advanced by modern machine learning (ML) techniques. While companies have successfully integrated HAR in…

eess.SP2024

Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling

Jingwei Zuo, Hakim Hacid

Human Activity Recognition (HAR) has been studied for decades, from data collection, learning models, to post-processing and result interpretations. However, the inherent hierarchy…

eess.SP2023

Practical Insights on Incremental Learning of New Human Physical Activity on the Edge

George Arvanitakis, Jingwei Zuo, Mthandazo Ndhlovu +1

Edge Machine Learning (Edge ML), which shifts computational intelligence from cloud-based systems to edge devices, is attracting significant interest due to its evident benefits in…

cs.LG2023

Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks

Jingwei Zuo, Wenbin Li, Michele Baldo +1

Air Quality Monitoring and Forecasting has been a popular research topic in recent years. Recently, data-driven approaches for air quality forecasting have garnered significant att…

cs.AI2023

Regularization of the policy updates for stabilizing Mean Field Games

Talal Algumaei, Ruben Solozabal, Reda Alami +3

This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual retu…