Non-Parametric Regression
Energy Based Process
Introduced by Yang et al. in Energy-Based Processes for Exchangeable DataEnergy Based Processes extend energy based models to exchangeable data while allowing neural network parameterizations of the energy function. They extend the previously separate stochastic process and latent variable model perspectives in a common framework. The result is a generalization of Gaussian processes and Student-t processes that exploits EBMs for greater flexibility.
Source: Energy-Based Processes for Exchangeable Data
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Denoising | 2 | 40.00% |
| Image Generation | 1 | 20.00% |
| Text-to-Image Generation | 1 | 20.00% |
| Point Cloud Generation | 1 | 20.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
