Tag: Neural

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Publisher ‏ : ‎ The MIT Press (August 17, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 454 pages
ISBN-10 ‏ : ‎ 0262542595
ISBN-13 ‏ : ‎ 978-0262542593
Item Weight ‏ : ‎ 1.88 pounds
Dimensions ‏ : ‎ 7.06 x 1.06 x 9.06 inches

Lifted probabilistic inference is a powerful technique for reasoning under uncertainty in complex graphical models. In this post, we will provide an introduction to lifted probabilistic inference and discuss how it can be applied in the context of neural information processing.

Graphical models are a popular framework for representing probabilistic relationships between variables in a complex system. In a graphical model, nodes represent random variables and edges represent dependencies between them. Probabilistic inference in graphical models involves calculating the probabilities of different variable assignments given observed evidence.

Traditional probabilistic inference algorithms, such as exact inference and approximate inference methods like Monte Carlo sampling, can be computationally expensive for large and densely connected graphical models. Lifted probabilistic inference offers a more efficient approach by exploiting symmetries and redundancies in the graphical model structure.

In lifted probabilistic inference, instead of performing inference on individual variables, we group variables into blocks and perform inference at the block level. This allows us to reduce the computational complexity of inference by aggregating information and exploiting shared computations.

Lifted probabilistic inference has been successfully applied in various domains, including computer vision, natural language processing, and computational biology. By leveraging the power of neural information processing, lifted probabilistic inference can further enhance the performance of neural networks in tasks requiring reasoning under uncertainty.

In conclusion, lifted probabilistic inference is a promising approach for efficient and scalable probabilistic reasoning in complex graphical models. By incorporating lifted inference techniques into neural information processing frameworks, researchers can achieve more accurate and robust predictions in a wide range of applications.
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