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We are excited to announce that Canv.ai now features a built-in translator, allowing you to communicate in your native language. You can write prompts in your language, and they will be automatically translated into English, facilitating communication and the exchange of ideas!

We value freedom of speech and guarantee the absence of censorship on Canv.ai. At the same time, we hope and believe in the high moral standards of our users, which will help maintain a respectful and constructive atmosphere.


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Markov Logic: An Interface Layer for Artificial Intelligence

Posted By: AvaxGenius
Markov Logic: An Interface Layer for Artificial Intelligence

Markov Logic: An Interface Layer for Artificial Intelligence by Pedro Domingos
English | PDF | 2009 | 155 Pages | ISBN : 1598296922 | 1.3 MB

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit.

Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models

Posted By: AvaxGenius
Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models

Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models by Antonino Freno
English | PDF | 2011 | 217 Pages | ISBN : 3642203078 | 2.61 MB

This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book–-rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.