Hierarchical Gaussian Process Models

Hierarchical Gaussian Process Models – Learning to predict future events is challenging because of the large, complex, and unpredictable nature of the data. Despite the enormous volume of available data, supervised learning has made great progress in recent years in learning to predict the future rather than in predicting the past. In this paper, we present a framework for modeling and predicting the future of data by non-Gaussian prior approximating latent Gaussian processes. The underlying assumptions are to be established in the context of non-Gaussian prior approximating learning, and we further elaborate on these assumptions in a neural-network architecture. We evaluate this network on two datasets: the Long Short-Term Memory and the Stanford Attention Framework dataset, where we show that the model achieves state-of-the-art performance with good accuracy.

This paper presents the first method for automatic and discriminative semantic segmentation of images. The method is based on convolutional networks (CNN), which first learns discriminative representations from images. The architecture based semantic segmentation (DSE) is very efficient and is often faster than the CNN model. In addition, the model learns a discriminative semantic segmentation metric, which is used to improve the discriminative representation of images. Moreover, we also learn a representation of the representation from the semantic segmentation metric. Experiments on two semantic segmentation datasets (MIS and TANGL) shows that the DSE learns discriminative semantic segmentation with strong performance.

Multiclass Super-Resolution with Conditional Generative Adversarial Networks

Leveraging the Observational Data to Identify Outliers in Ensembles

Hierarchical Gaussian Process Models

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  • Nonparametric Bayesian Optimization

    Structural Matching NetworksThis paper presents the first method for automatic and discriminative semantic segmentation of images. The method is based on convolutional networks (CNN), which first learns discriminative representations from images. The architecture based semantic segmentation (DSE) is very efficient and is often faster than the CNN model. In addition, the model learns a discriminative semantic segmentation metric, which is used to improve the discriminative representation of images. Moreover, we also learn a representation of the representation from the semantic segmentation metric. Experiments on two semantic segmentation datasets (MIS and TANGL) shows that the DSE learns discriminative semantic segmentation with strong performance.


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