2 citations · 2 across the 5 of their papers we have counts for
6 papers
LeanTTA: A Backpropagation-Free and Stateless Approach to Quantized Test-Time Adaptation on Edge Devices
Cynthia Dong, Hong Jia, Young D. Kwon +2
While there are many advantages to deploying machine learning models on edge devices, the resource constraints of mobile platforms, the dynamic nature of the environment, and diffe…
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
Yu Wu, Ting Dang, Dimitris Spathis +2
Contrastive learning (CL) has emerged as a promising approach for representation learning in time series data by embedding similar pairs closely while distancing dissimilar ones. H…
UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers
Hong Jia, Young D. Kwon, Dong Ma +4
Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phas…
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
Young D. Kwon, Jagmohan Chauhan, Hong Jia +2
Continual Learning (CL) allows applications such as user personalization and household robots to learn on the fly and adapt to context. This is an important feature when context, a…
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction
Yu Wu, Dimitris Spathis, Hong Jia +5
Deep learning models have shown great promise in various healthcare monitoring applications. However, most healthcare datasets with high-quality (gold-standard) labels are small-sc…
Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction
Yu Wu, Dimitris Spathis, Hong Jia +5
Deep learning models have shown great promise in various healthcare applications. However, most models are developed and validated on small-scale datasets, as collecting high-quali…