Graph-based time-series anomaly detection: A survey
With the recent advances in technology, a wide range of systems continue to collect a large
amount of data over time and thus generate time series. Time-Series Anomaly Detection …
amount of data over time and thus generate time series. Time-Series Anomaly Detection …
Universal time-series representation learning: A survey
Time-series data exists in every corner of real-world systems and services, ranging from
satellites in the sky to wearable devices on human bodies. Learning representations by …
satellites in the sky to wearable devices on human bodies. Learning representations by …
[HTML][HTML] Learning transactions representations for information management in banks: Mastering local, global, and external knowledge
In today's world, banks use artificial intelligence to optimize diverse business processes,
aiming to improve customer experience. Most of the customer-related tasks can be …
aiming to improve customer experience. Most of the customer-related tasks can be …
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration
We introduce ColaCare, a framework that enhances Electronic Health Record (EHR)
modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our …
modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our …
CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised Pretraining
The increasing availability of low-cost wearable devices and smartphones has significantly
advanced the field of sensor-based human activity recognition (HAR), attracting …
advanced the field of sensor-based human activity recognition (HAR), attracting …
An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data
Traffic data imputation is a critical preprocessing step in intelligent transportation systems,
enabling advanced transportation services. Despite significant advancements in this field …
enabling advanced transportation services. Despite significant advancements in this field …
DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series
The real-world Electronic Health Records (EHRs) present irregularities due to changes in
the patient's health status, resulting in various time intervals between observations and …
the patient's health status, resulting in various time intervals between observations and …
Unleashing the Power of Shared Label Structures for Human Activity Recognition
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 …
IDs without explicitly modeling the semantics of class labels. We observe that different …
TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning
We propose TAMER, a Test-time Adaptive MoE-driven framework for EHR Representation
learning. TAMER combines a Mixture-of-Experts (MoE) with Test-Time Adaptation (TTA) to …
learning. TAMER combines a Mixture-of-Experts (MoE) with Test-Time Adaptation (TTA) to …
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series
Ordinary Differential Equations (ODEs) based models have become popular as foundation
models for solving many time series problems. Combining neural ODEs with traditional RNN …
models for solving many time series problems. Combining neural ODEs with traditional RNN …