shubhomoydas/ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
PythonMIT
Stargazers
- abhimalhotra00
- AeroOwl
- benlongoVALIS Insights, Inc.
- brightgemsShanghai
- emilleishidaCNRS - Laboratoire de Physique de Clermont
- esevastyanov
- faducoder
- h4nyu
- jp629
- jtgarrityBoston, MA
- jwhellandUS
- kazparDomin
- legomushroomSeattle, US
- LingxiaoShawn
- linxidalibaba
- Lorrainexun
- lytkarinskiySIBUR
- minkcho
- Modakeo
- nekruzvatanshoev
- nicococosheetcloud.org
- oeeckhoutteLean Deep
- rislamMD Anderson Cancer Center
- rjcampa
- rnetonetBrazil
- saikatkumardey@Gojek
- sfmeUniversity of Edinburgh
- smalikPlytos Inc.
- smrjansTalentica
- sunoonlee
- taopengpengtao
- tap222Things Alive
- tsatolol
- tung6192Hanoi, Vietnam
- xiaoningwangCommunication University of China
- zhuwzh