Search Results for author:
Found 9 papers, 7 papers with code
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals.
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration.
Deep Isolation Forest for Anomaly Detection
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability.
Ranked #1 on
Anomaly Detection
on NB15-DoS
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers.
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning
Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks.
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data
Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data.
Ranked #1 on
Outlier Detection
on Hepatitis
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data
Non-IID categorical data are ubiquitous and distinct in real-world applications.
