Search Results for author:
Found 5 papers, 5 papers with code
Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks
In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks.
Semi-supervised Time Series Classification by Temporal Relation Prediction
Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner.
Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning
In this paper, we present SelfTime: a general self-supervised time series representation learning framework, by exploring the inter-sample relation and intra-temporal relation of time series to learn the underlying structure feature on the unlabeled time series.
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection
Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications.
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings.
