Summary of On Faster Marginalization with Squared Circuits Via Orthonormalization, by Lorenzo Loconte et al.
On Faster Marginalization with Squared Circuits via Orthonormalization
by Lorenzo Loconte, Antonio Vergari
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a new approach to simplify the computation of distribution estimators in high-dimensional spaces using squared tensor networks (TNs) and their generalization as parameterized computational graphs, also known as squared circuits. These models have been shown to be expressive but introducing additional complexity when marginalizing variables or computing partition functions, hindering their application in machine learning. The authors propose a new parameterization that ensures encoded distributions are already normalized, leading to an efficient algorithm for computing any marginal of these circuits without loss of expressiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it easier to use special math tools called tensor networks and computational graphs. These tools can help us understand big data sets, but they can be tricky to work with because they have many parts that need to be figured out. The authors came up with a new way to simplify these calculations so we can use the tools more easily. This could help us make better predictions and decisions in machine learning. |
Keywords
» Artificial intelligence » Generalization » Machine learning
