Approximate Inference
Approximate Bayesian Computation
Introduced by Kulkarni et al. in Accelerating Simulation-based Inference with Emerging AI HardwareClass of methods in Bayesian Statistics where the posterior distribution is approximated over a rejection scheme on simulations because the likelihood function is intractable.
Different parameters get sampled and simulated. Then a distance function is calculated to measure the quality of the simulation compared to data from real observations. Only simulations that fall below a certain threshold get accepted.
Image source: Kulkarni et al.
Source: Accelerating Simulation-based Inference with Emerging AI Hardware
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Music Generation | 3 | 4.23% |
| CAD Reconstruction | 3 | 4.23% |
| Decision Making | 3 | 4.23% |
| Semantic Segmentation | 2 | 2.82% |
| Point cloud reconstruction | 2 | 2.82% |
| Information Retrieval | 2 | 2.82% |
| Music Information Retrieval | 2 | 2.82% |
| Retrieval | 2 | 2.82% |
| Classification | 2 | 2.82% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
