Summary of Lifted Model Construction Without Normalisation: a Vectorised Approach to Exploit Symmetries in Factor Graphs, by Malte Luttermann et al.
Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs
by Malte Luttermann, Ralf Möller, Marcel Gehrke
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed algorithm generalizes the state-of-the-art Advanced Colour Passing (ACP) method to construct parametric factor graphs. It efficiently detects more symmetries by allowing potentials of factors to be scaled arbitrarily, leading to a more compact representation. This improvement significantly reduces online query times for probabilistic inference, as confirmed in experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves the current state-of-the-art algorithm for constructing lifted representations in parametric factor graphs. It makes the model more efficient and accurate by detecting more symmetries between factors. This is important because it can be applied to many areas where we need to make predictions or decisions based on uncertain information. |
Keywords
* Artificial intelligence * Inference
