Search Results for author: Khoa T. Phan

Found 5 papers, 2 papers with code

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values

no code implementations11 Jan 2024 Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa T. Phan, Yi-Ping Phoebe Chen, Shirui Pan, Wei Xiang

However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values.

Anomaly Detection Time Series +1

Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

1 code implementation17 Jul 2023 Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xiang

To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time series anomaly detection.

Anomaly Detection Graph Learning +2

Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

no code implementations27 Feb 2023 Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang, Khoa T. Phan, Eryk Dutkiewicz, Dusit Niyato, Tao Shu

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before.

reinforcement-learning Reinforcement Learning (RL)

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

1 code implementation23 Aug 2021 Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen

While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.

Attribute Contrastive Learning +3

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