PW
Machine-Learning-Tokyo/papers-with-annotations
Research papers with annotations, illustrations and explanations
831 74 +0/wk
GitHub
computer-vision deep-learning machine-learning
Trend
0
Star & Fork Trend (32 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
Machine-Learning-Tokyo/papers-with-annotations has +0 stars this period . Velocity data will be available after more historical data is collected.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | papers-with-annotations | HGNN | elki | human-learn |
|---|---|---|---|---|
| Stars | 831 | 831 | 831 | 830 |
| Forks | 74 | 157 | 324 | 56 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | N/A | Python | Java | Jupyter Notebook |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | MIT | AGPL-3.0 | MIT |
Capability Radar vs HGNN
papers-with-annotations
HGNN
Maintenance Activity 0
Last code push 1825 days ago.
Community Engagement 45
Fork-to-star ratio: 8.9%. Lower fork ratio may indicate passive usage.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.