most citedLarge Language Models for Time Series: A Survey

9 citations · 14 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SP2024★ 1 cited

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…

cs.CV2024

Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization

Han Guo, Ramtin Hosseini, Ruiyi Zhang +4

Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning. It operates by randomly masking image patches and reconstructing the…

cs.LG2024★ 9 cited

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…

cs.LG2023

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…

cs.LG2023★ 1 cited

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,…

cs.LG2023★ 3 cited

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…