DmitryRyumin/AAAI-2024-Papers
AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for better understanding. ⭐ experience the forefront of progress in artificial intelligence with this repository!
Star & Fork Trend (23 data points)
Multi-Source Signals
Growth Velocity
DmitryRyumin/AAAI-2024-Papers has +0 stars this period . Velocity data will be available after more historical data is collected.
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Signal-backed technical analysis will be available soon.
| Metric | AAAI-2024-Papers | dropblock | FastAI.jl | ChangeFormer |
|---|---|---|---|---|
| Stars | 593 | 594 | 592 | 592 |
| Forks | 26 | 94 | 49 | 80 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Python | Python | Julia | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | MIT | MIT | MIT |
Capability Radar vs dropblock
Last code push 437 days ago.
Fork-to-star ratio: 4.4%. Lower fork ratio may indicate passive usage.
Issue data not yet available.
No measurable growth in the current period (first-day cold start expected).
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.