Generalized Belief Propagation with Randomized Projections


Generalized Belief Propagation with Randomized Projections – Generative adversarial network (GAN) has received much attention recently.GAN has been shown to capture more information in the input images than other baselines and offers great success on many classification problems. However, the large number of classification datasets required to learn the underlying model has never been addressed in large datasets. This paper addresses this issue with Generative adversarial network (GAN) using a novel dataset structure called S-1-Mixture. A network is constructed with two branches where each branch contains all training data and the other branches contains data for classification. We use the two branches to separate the data and to extract the most relevant ones. The objective of the network is to achieve high classification accuracy and high classification speed in a large dataset with a high number of classification tasks. Experimental results on both public domain datasets demonstrate that the proposed method results in significant improvements over a state-of-the-art GAN model trained on publicly available datasets.

This work presents a method allowing an information theoretic system model to extract high-dimensional representations of the data. We provide a principled, efficient algorithm for this task, and a methodology for optimizing the algorithm’s performance. We present a detailed study of the proposed algorithm, which shows that it yields significantly better performance on both synthetic and real data.

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Generalized Belief Propagation with Randomized Projections

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  • Adversarial Learning for Brain-Computer Interfacing: A Survey

    Bayesian Nonparanormal ClusteringThis work presents a method allowing an information theoretic system model to extract high-dimensional representations of the data. We provide a principled, efficient algorithm for this task, and a methodology for optimizing the algorithm’s performance. We present a detailed study of the proposed algorithm, which shows that it yields significantly better performance on both synthetic and real data.


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