Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Inference in large scale graphical models is an important task in many domains, and in particular probabilistic relational models (e.g. Markov logic networks). Such models often exhibit considerable symmetry, and it is a challenge to devise algorithms that exploit this symmetry to speed up inference. Recently, the automorphism group has been proposed to formalize mathematically what "exploiting symmetry" means.#R##N##R##N#Hover, obtaining symmetry derived from automorphism is GI-hard, and consequently only a small fraction of the symmetry is easily available for effective employment. ,  improve upon efficiency in two ways. First,  introduce the Cluster Signature Graph (CSG), a platform on which greater portions of the symmetries can be revealed and exploited. CSGs classify clusters of variables by projecting relations beten cluster members onto a graph, allowing for the efficient pruning of symmetrical clusters even before their generation. Second,  introduce a novel framework based on CSGs for the Sherali-Adams hierarchy of linear program (LP) relaxations, dedicated to exploiting this symmetry for the benefit of tight Maximum A Posteriori (MAP) approximations. Combined with the pruning por of CSG, the framework quickly generates compact formulations for otherwise intractable LPs, as demonstrated by several empirical results.
['Udi Apsel', 'Kristian Kersting', 'Martin Mladenov']
I am writing a research paper on a idea Lifting relational MAP-LPs using cluster signatures
[{"from": "human", "value": "I am writing a research paper on a idea Lifting relational MAP-LPs using cluster signatures"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Inference in large scale graphical models is an important task in many domains, and in particular probabilistic relational models (e.g. Markov logic networks). Such models often exhibit considerable symmetry, and it is a challenge to devise algorithms that exploit this symmetry to speed up inference. Recently, the automorphism group has been proposed to formalize mathematically what \"exploiting symmetry\" means.#R##N##R##N#Hover, obtaining symmetry derived from automorphism is GI-hard, and consequently only a small fraction of the symmetry is easily available for effective employment. ,  improve upon efficiency in two ways. First,  introduce the Cluster Signature Graph (CSG), a platform on which greater portions of the symmetries can be revealed and exploited. CSGs classify clusters of variables by projecting relations beten cluster members onto a graph, allowing for the efficient pruning of symmetrical clusters even before their generation. Second,  introduce a novel framework based on CSGs for the Sherali-Adams hierarchy of linear program (LP) relaxations, dedicated to exploiting this symmetry for the benefit of tight Maximum A Posteriori (MAP) approximations. Combined with the pruning por of CSG, the framework quickly generates compact formulations for otherwise intractable LPs, as demonstrated by several empirical results."}]