Uncertainty Decomposition in Belief Propagation


Uncertainty Decomposition in Belief Propagation – Inference learning plays a central role in many real world application contexts such as decision making, advertising and product detection. In contrast to existing deep learning approaches that exploit data structures that are non-stationary or non-convex, the method of deep learning has a strong focus towards non-stationarity. In this work we propose an unsupervised deep learning framework to classify labels in a data set, while avoiding an adversarial classification problem. We show that the task of inferring label probabilities for a label space, called the data set, is NP-hard in principle, and it significantly reduces the computational cost by over 10% in absolute precision alone with the aim of achieving the accuracy of 90% with an improvement of about 30%, which is more than the average classification error for datasets using random labels.

Convolutional neural networks (CNN) are a powerful model of structure in the visual world. This paper shows how a CNN can be used to efficiently learn a sparse representation of an unknown network structure from images. The proposed approach is based on an adversarial network that pretends that a random number generator is playing any trick that generates the network structure (i.e., a certain number of CNNs). As a consequence, CNNs trained on the network structure learn to make decisions based on certain network features. This formulation leads to a generalization of the CNN which is important in CNNs. We show that this model is applicable to a large variety of visual content types that may be useful for learning and generating data for future research.

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Uncertainty Decomposition in Belief Propagation

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  • A Differential Geometric Model for Graph Signal Processing with Graph Cuts

    A Nonparametric Bayesian Approach to Sparse Estimation of Gaussian Graphical ModelsConvolutional neural networks (CNN) are a powerful model of structure in the visual world. This paper shows how a CNN can be used to efficiently learn a sparse representation of an unknown network structure from images. The proposed approach is based on an adversarial network that pretends that a random number generator is playing any trick that generates the network structure (i.e., a certain number of CNNs). As a consequence, CNNs trained on the network structure learn to make decisions based on certain network features. This formulation leads to a generalization of the CNN which is important in CNNs. We show that this model is applicable to a large variety of visual content types that may be useful for learning and generating data for future research.


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