Summary of Probabilistic Inference in the Era Of Tensor Networks and Differential Programming, by Martin Roa-villescas et al.
Probabilistic Inference in the Era of Tensor Networks and Differential Programming
by Martin Roa-Villescas, Xuanzhao Gao, Sander Stuijk, Henk Corporaal, Jin-Guo Liu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Physics (physics.comp-ph)
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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 presents advances in exact probabilistic inference methods by formulating and implementing tensor-based solutions for various inference tasks in probabilistic graphical models. Specifically, it develops techniques for computing partition functions, marginal probabilities, most likely assignments, and sampling from learned probability distributions. The work is motivated by recent advances in quantum circuit simulation, quantum many-body physics, and statistical physics. An experimental evaluation shows that the integration of these quantum technologies with the proposed algorithms significantly improves the effectiveness of existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it easier to solve some important problems in machine learning by using special math techniques called tensor networks. It finds new ways to do four different types of calculations: computing a value that represents the total amount of probability, finding probabilities for groups of variables, determining the most likely values for a group of variables, and generating random samples from a learned probability distribution. This work is important because it can help improve how well we can solve these types of problems. |
Keywords
» Artificial intelligence » Inference » Machine learning » Probability
