The Dynamics of Hidden Variables in Conditional Independence Distributions


The Dynamics of Hidden Variables in Conditional Independence Distributions – We present an extension to the Conditional Independence Process (CI) that allows us to make predictions about the distribution of a latent variable given a posterior. The CI is a generalization of the classical CI, and therefore a nonparametric probabilistic estimator for the conditional independence. We study these probabilistic estimators on the CINF and CIFAR-10 datasets. By training the CI, we learn more about the conditional independence between latent variables, allowing for faster inference in terms of the latent parameters. We also propose an efficient inference algorithm based on Bayesian networks for this problem. Using the CI, we also design a new inference algorithm which approximates the Cinformatrix function in an empirical manner. We validate the proposed method on data sets with high latent variables in order to verify its potential.

Video has been used to create the illusion of being human-like to a large extent, yet this may not be able to provide a good model of the human personality. Recently a new approach to model human intelligence (HIT) called the Self-Organizing System (SA) has been proposed to understand the self-organizing power of video. Here, we propose a new model that has a direct representation of the human personality, and its ability to generate videos through a learned network of attention mechanisms that are a key to its intelligence. The proposed model has the ability to automatically learn a new video model from its previous learning process, and adapt to its new video data. Experimental results on a variety of real-world videos show that the proposed model generates the same and more human-like video than previous models.

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The Dynamics of Hidden Variables in Conditional Independence Distributions

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    Fully Parallel Supervised LAD-SLAM for Energy-Efficient Applications in Video ProcessingVideo has been used to create the illusion of being human-like to a large extent, yet this may not be able to provide a good model of the human personality. Recently a new approach to model human intelligence (HIT) called the Self-Organizing System (SA) has been proposed to understand the self-organizing power of video. Here, we propose a new model that has a direct representation of the human personality, and its ability to generate videos through a learned network of attention mechanisms that are a key to its intelligence. The proposed model has the ability to automatically learn a new video model from its previous learning process, and adapt to its new video data. Experimental results on a variety of real-world videos show that the proposed model generates the same and more human-like video than previous models.


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