2 citations · 3 across the 10 of their papers we have counts for
12 papers
Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?
Yutong Feng, Bowen Liao, See Kiong Ng +1
Joint-embedding predictive architectures (JEPA) have emerged as a promising self-supervised pretraining paradigm for time series, learning representations by predicting target embe…
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
Yutong Feng, Shiyuan Piao, Yutong Xia +5
Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from di…
Activation Steering Induces Emergent Misalignment: Steering Vector Construction and Comprehensive Evaluation
Qi Cao, Jian Lou, Meiting Liu +4
Activation steering (AS) has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). AS modulates model behavior in a flexible an…
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
Shunyu Wu, Dan Li, Haozheng Ye +6
Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs…
Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification
Jiahui Li, Jianfeng Shan, Wenpei Chen +5
Test-time reinforcement learning has emerged as a promising paradigm for enhancing the complex reasoning abilities of large language models in a completely label-free manner. Despi…
MsFormer: Enabling Robust Predictive Maintenance Services for Industrial Devices
Jiahui Zhou, Dan Li, Ruibing Jin +5
Providing reliable predictive maintenance is a critical industrial AI service essential for ensuring the high availability of manufacturing devices. Existing deep-learning methods…
Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
Jiahui Zhou, Dan Li, Boxin Li +6
Time series is a pervasive data type across various application domains, rendering the reasonable solving of diverse time series tasks a long-standing goal. Recent advances in larg…
Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
Shunyu Wu, Tianyue Li, Yixuan Leng +4
Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the q…
Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting
Zhuomin Chen, Dan Li, Jiahui Zhou +4
Time series (TS) data are ubiquitous across various application areas, rendering time series forecasting (TSF) a fundamental task. With the astounding advances in large language mo…
Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment
Shunyu Wu, Dan Li, Wenjie Feng +3
High-quality time series (TS) data are essential for ensuring TS model performance, rendering research on rating TS data quality indispensable. Existing methods have shown promisin…
GLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series
Gang Tu, Dan Li, Bingxin Lin +2
Unlike images and natural language tokens, time series data is highly semantically sparse, resulting in labor-intensive label annotations. Unsupervised and Semi-supervised Domain A…
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding
Xian Wei, See Kiong Ng, Tongtong Zhang +1
Patient similarity assessment (PSA) is pivotal to evidence-based and personalized medicine, enabled by analyzing the increasingly available electronic health records (EHRs). Howeve…