9 citations · 15 across the 8 of their papers we have counts for
8 papers
Matching Skeleton-based Activity Representations with Heterogeneous Signals for HAR
Shuheng Li, Jiayun Zhang, Xiaohan Fu +3
In human activity recognition (HAR), activity labels have typically been encoded in one-hot format, which has a recent shift towards using textual representations to provide contex…
Orthogonal Calibration for Asynchronous Federated Learning
Jiayun Zhang, Shuheng Li, Haiyu Huang +3
Asynchronous federated learning mitigates the inefficiency of conventional synchronous aggregation by integrating updates as they arrive and adjusting their influence based on stal…
UniMTS: Unified Pre-training for Motion Time Series
Xiyuan Zhang, Diyan Teng, Ranak Roy Chowdhury +4
Motion time series collected from mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications…
Large Language Models for Time Series: A Survey
Xiyuan Zhang, Ranak Roy Chowdhury, Rajesh K. Gupta +1
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a sig…
Physics-Informed Data Denoising for Real-Life Sensing Systems
Xiyuan Zhang, Xiaohan Fu, Diyan Teng +9
Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reli…
Towards Diverse and Coherent Augmentation for Time-Series Forecasting
Xiyuan Zhang, Ranak Roy Chowdhury, Jingbo Shang +2
Time-series data augmentation mitigates the issue of insufficient training data for deep learning models. Yet, existing augmentation methods are mainly designed for classification,…
Unleashing the Power of Shared Label Structures for Human Activity Recognition
Xiyuan Zhang, Ranak Roy Chowdhury, Jiayun Zhang +3
Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different…
WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability
Yufan Zhuang, Zihan Wang, Fangbo Tao +1
Transformer and its variants are fundamental neural architectures in deep learning. Recent works show that learning attention in the Fourier space can improve the long sequence lea…