32 citations · 92 across the 21 of their papers we have counts for
8 papers · 1 filter
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…
RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction
Yuwei Zhang, Tong Xia, Aaqib Saeed +1
The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening. Machine learning models can automate clinical consultatio…
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning
Jialu Tang, Tong Xia, Yuan Lu +2
Electrocardiogram (ECG) interpretation requires specialized expertise, often involving synthesizing insights from ECG signals with complex clinical queries posed in natural languag…
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
Jialu Tang, Tong Xia, Yuan Lu +2
Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time inves…
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
Tong Xia, Abhirup Ghosh, Xinchi Qiu +1
Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing…
Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking
Yuwei Zhang, Tong Xia, Jing Han +6
Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for th…
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…
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
Chi Ian Tang, Lorena Qendro, Dimitris Spathis +3
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature…