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The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
Adbench: Anomaly detection benchmark
Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
An overview on deep clustering
X Wei, Z Zhang, H Huang, Y Zhou - Neurocomputing, 2024 - Elsevier
In recent years, with the great success of deep learning and especially deep unsupervised
learning, many deep architectural clustering methods, collectively known as deep clustering …
learning, many deep architectural clustering methods, collectively known as deep clustering …
Achieving fairness at no utility cost via data reweighing with influence
With the fast development of algorithmic governance, fairness has become a compulsory
property for machine learning models to suppress unintentional discrimination. In this paper …
property for machine learning models to suppress unintentional discrimination. In this paper …
Deep weakly-supervised anomaly detection
Recent semi-supervised anomaly detection methods that are trained using small labeled
anomaly examples and large unlabeled data (mostly normal data) have shown largely …
anomaly examples and large unlabeled data (mostly normal data) have shown largely …
Robust fair clustering: A novel fairness attack and defense framework
Clustering algorithms are widely used in many societal resource allocation applications,
such as loan approvals and candidate recruitment, among others, and hence, biased or …
such as loan approvals and candidate recruitment, among others, and hence, biased or …
Supervised algorithmic fairness in distribution shifts: A survey
Supervised fairness-aware machine learning under distribution shifts is an emerging field
that addresses the challenge of maintaining equitable and unbiased predictions when faced …
that addresses the challenge of maintaining equitable and unbiased predictions when faced …
Learning antidote data to individual unfairness
Fairness is essential for machine learning systems deployed in high-stake applications.
Among all fairness notions, individual fairness, deriving from a consensus that 'similar …
Among all fairness notions, individual fairness, deriving from a consensus that 'similar …
Self-supervised deep clustering method for detecting abnormal data of wastewater treatment process
In wastewater treatment process (WWTP), abnormal data seriously reduce data quality
rendering the application techniques impractical. The implementation of abnormal data …
rendering the application techniques impractical. The implementation of abnormal data …
Achieving counterfactual fairness for anomaly detection
Ensuring fairness in anomaly detection models has received much attention recently as
many anomaly detection applications involve human beings. However, existing fair anomaly …
many anomaly detection applications involve human beings. However, existing fair anomaly …