Search Results for author:
Found 11 papers, 3 papers with code
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection
The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data.
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone
The selection is based on the user's preferences regarding anomaly detection accuracy and computational cost.
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
Graph Anomaly Detection
Semi-supervised Anomaly Detection
+1
Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification
Since the launch of various generative AI tools, scientists have been striving to evaluate their capabilities and contents, in the hope of establishing trust in their generative abilities.
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems.
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream
Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based infrastructures.
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Time-series data are ubiquitous these days, but lack of the labels in time-series data is regarded as a hurdle for its broad applicability.
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations
We showed that for similar accuracy, the speed-up achieved for SOMTimeS and K-means was 1. 8x on average; however, rates varied between 1x and 18x depending on the dataset.
A Benchmark Study on Time Series Clustering
This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive -- the state of the art repository of time series data.
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering
Additionally, using available meteorological data associated with storm events, we examine the characteristics of computational clusters of storm events in the study watersheds and identify the features driving the clustering approach.
Real-time Top-K Predictive Query Processing over Event Streams
We overcome these limitations by proposing a novel event precedence model and a run-time causal inference mechanism.
