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 |
|---|---|---|
| GPR | 40 | 13.79% |
| Active Learning | 24 | 8.28% |
| Time Series | 23 | 7.93% |
| Decision Making | 22 | 7.59% |
| Bayesian Optimisation | 12 | 4.14% |
| Dimensionality Reduction | 9 | 3.10% |
| Meta-Learning | 8 | 2.76% |
| Experimental Design | 7 | 2.41% |
| Density Estimation | 5 | 1.72% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
