add trainable normalized cut
Open
new-feature
- Dominant language
- Python
- Stars
- 30
- Forks
- 9
- PR merge metrics
- No merged PRs in 30d
Description
Timothée Cour, Nicolas Gogin, and Jianbo Shi have a [paper](http://www.cis.upenn.edu/~jshi/papers/AISTATS2005.pdf) describing how to learn a spectral graph segmentation function. This could be implemented in ray.
Contributor guide
No contributing guide indexed for this repository
Research direction
No files, tests, or entry points are named. Start by reading the linked AISTATS 2005 paper and inspecting the ray package structure to identify the relevant segmentation entry point. Done means agreeing on the intended trainable normalized-cut behavior and implementing it with coverage for the expected use case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100