15 citations · 28 across the 27 of their papers we have counts for
6 papers · 1 filter
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Mengxuan Li, Ke Liu, Hongyang Chen +3
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this t…
Attention Beats Linear for Fast Implicit Neural Representation Generation
Shuyi Zhang, Ke Liu, Jingjun Gu +4
Implicit Neural Representation (INR) has gained increasing popularity as a data representation method, serving as a prerequisite for innovative generation models. Unlike gradient-b…
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
Zhonghao Wang, Danyu Sun, Sheng Zhou +4
Graph Neural Networks (GNNs) exhibit strong potential in node classification task through a message-passing mechanism. However, their performance often hinges on high-quality node…
Towards a Unified Framework of Clustering-based Anomaly Detection
Zeyu Fang, Ming Gu, Sheng Zhou +4
Unsupervised Anomaly Detection (UAD) plays a crucial role in identifying abnormal patterns within data without labeled examples, holding significant practical implications across v…
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Jinke Shi +3
Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real…
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Zhen Zhang, Meihan Liu, Anhui Wang +4
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. Howeve…