Automatic Unsupervised Outlier Model Selection

Revisiting Time Series Outlier Detection: Definitions and Benchmarks

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection

Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection. Due to the lack of ground truth labels, practitioners …

TODS: An Automated Time Series Outlier Detection System

A Data Denoising Approach to Optimize Functional Clustering of Single Cell RNA-sequencing Data

AutoAudit: Mining Accounting and Time-Evolving Graphs

COPOD: Copula-Based Outlier Detection

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing unsupervised approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As …

SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly. Although it is a fundamental step for many data science …

DSR: An Accurate Single Image Super Resolution Approach for Various Degradations

Recently, convolution neural networks based approaches have achieved unprecedented success for image super resolution. However, such methods typically assume a predetermined degradation that deviates from real-world cases, resulting in poor …