Class 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 HardwarePaper | Code | Results | Date | Stars |
---|
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% |
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |