LA
aleximmer/Laplace
Laplace approximations for Deep Learning.
538 89 +0/wk
GitHub
approximate-bayesian-inference deep-learning laplace-approximation neural-network
Trend
0
Star & Fork Trend (36 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
aleximmer/Laplace 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 | Laplace | torch-light | ner-lstm | Gradient-Centralization |
|---|---|---|---|---|
| Stars | 538 | 538 | 538 | 539 |
| Forks | 89 | 195 | 180 | 80 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Python | Python | Python | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | MIT | N/A | N/A |
Capability Radar vs torch-light
Laplace
torch-light
Maintenance Activity 0
Last code push 352 days ago.
Community Engagement 83
Fork-to-star ratio: 16.5%. Active community forking and contributing.
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.