yzhao062 / SUOD
(MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)
Why this job: works in the areas we cover, but does not say which job it does
What it does
It can be easily invoked in PyOD by following the SUOD example _. In a nutshell, we could easily initialize a few outlier detectors and then use SUOD for collective training and prediction!
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 2 months ago
- Stars
- 395
- +1 this week
- Licence
- BSD-2-Clause
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 25% closed
- 9 still open
- Pull requests
- 0 merged
- 2 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 100%
Topics
- data-mining
- machine-learning
- anomaly-detection
- outlier-detection
- distributed-systems
- knowledge-distillation
- python
- machine-learning-algorithms
- machine-learning-library
Project record
- JobOther AI tools
- StatusListed
- Commits171
- Forks47
- Watching15
- Stars in 28 days+1
- CreatedNov 20, 2019
- First seen by usNov 7, 2026
- CheckedNov 8, 2026