Non-Parametric Regression
Gaussian Process
Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.
Image Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Bayesian Optimization | 58 | 15.26% |
| GPR | 44 | 11.58% |
| Active Learning | 26 | 6.84% |
| Time Series Analysis | 18 | 4.74% |
| Decision Making | 16 | 4.21% |
| Classification | 10 | 2.63% |
| BIG-bench Machine Learning | 10 | 2.63% |
| Meta-Learning | 8 | 2.11% |
| Dimensionality Reduction | 7 | 1.84% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
