Description
TensorInference, a package for exact probabilistic inference in discrete graphical models, capitalizes on recent tensor network advancements. Its tensor-based engine features optimized contraction ordering methods, an aspect vital to computational performance. Additionally, it incorporates optimized BLAS routines and GPU technology for enhanced efficiency. In a comparative evaluation with similar libraries, TensorInference demonstrates superior scalability for models of increasing complexity.| Period | 10 Jul 2024 |
|---|---|
| Event title | JuliaCon 2024 |
| Event type | Conference |
| Location | Eindhoven, NetherlandsShow on map |
| Degree of Recognition | International |
Keywords
- probabilistic inference
- tensor networks
- computational efficiency
Documents & Links
Related content
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Research output
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TensorInference: A Julia package for tensor-based probabilistic inference
Research output: Contribution to journal › Article › Academic › peer-review