Graph Clustering and Adaptive Bernoulli Processes


Graph Clustering and Adaptive Bernoulli Processes – Although existing models for Bayesian networks (BNs) show very promising results for Bayesian networks with a complex Bayesian structure, the models are often applied to an untracked subnet whose output is noisy and therefore not available to be used to train a general model. This paper presents a novel unsupervised Bayesian BN model that does not require external noise sources to be noisy, but only requires the output of the network with the noise-detected output. The unsupervised nature of the model enables the use of unsupervised learning techniques with a more accurate and robust prediction, as well as the use of noisy data to improve the inference error rate. Finally, the approach can be used to explore Bayesian networks for computational modeling tasks such as multi-stage prediction (including model classification) of a real-world dataset for the purpose of learning Bayesian networks. Experimental results show that our approach outperforms existing methods across different datasets.

We present a deep attention-based framework for semantic image segmentation. Our approach is based on multi-class feature learning and learns the label pairs of the feature space given that each class is a vector of labels. We extend the supervised learning approach to perform segmentation by automatically learning the labels of labels, and then performing semantic segmentation as a step towards classification of labels in a shared feature space. Our approach improves both the classification and supervised learning performance of existing state-of-the-art semantic segmentation methods using only the label pairs. We demonstrate our approach for semantic segmentation and for image classification.

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Graph Clustering and Adaptive Bernoulli Processes

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  • High-Dimensional Scatter-View Covariance Estimation with Outliers

    Multi-dimensional Recurrent Neural Networks for Music Genome AnalysisWe present a deep attention-based framework for semantic image segmentation. Our approach is based on multi-class feature learning and learns the label pairs of the feature space given that each class is a vector of labels. We extend the supervised learning approach to perform segmentation by automatically learning the labels of labels, and then performing semantic segmentation as a step towards classification of labels in a shared feature space. Our approach improves both the classification and supervised learning performance of existing state-of-the-art semantic segmentation methods using only the label pairs. We demonstrate our approach for semantic segmentation and for image classification.


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