Predictive Energy Approximations with Linear-Gaussian Measures


Predictive Energy Approximations with Linear-Gaussian Measures – We present an effective approach for estimating the mutual information contained in a data set. We study the problem of predicting the mutual information in a data set from a model using a Gaussian mixture model (FDM). We define a new, efficient, and very general model that can be used as the model for the prediction problem. We demonstrate that our method yields a model for predicting the mutual information in a data set.

An important aspect of neural inference is its ability to handle complex networks. In this work, we investigate the possibility to extend this framework with a new type of factorization-based feature learning (FML) in which a new dimension is associated with each node. We show that FML is the first such framework to learn the relevant features on a large scale using the new dimension. By using the new dimension, we further demonstrate the ability of FML to produce meaningful features of high dimension. To this end, we demonstrate the potential of FML on the problem of visual content classification.

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Predictive Energy Approximations with Linear-Gaussian Measures

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  • Learning Discriminative Feature Representations with Structural Priors for Robust and Efficient Mobile Location Analytics

    Covariance matrices for graph Laplacian featuresAn important aspect of neural inference is its ability to handle complex networks. In this work, we investigate the possibility to extend this framework with a new type of factorization-based feature learning (FML) in which a new dimension is associated with each node. We show that FML is the first such framework to learn the relevant features on a large scale using the new dimension. By using the new dimension, we further demonstrate the ability of FML to produce meaningful features of high dimension. To this end, we demonstrate the potential of FML on the problem of visual content classification.


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