Learning Graphs from Continuous Time and Space Variables


Learning Graphs from Continuous Time and Space Variables – We propose an efficient and robust optimization algorithm for training Bayesian networks. We show several theoretical bounds on the Bayesian framework. Our algorithm is competitive with the state-of-the-art approaches and outperforms them. Moreover, we show how other methods, including the ones used in the literature, can be improved.

In this work we present a novel recurrent neural network architecture, Residual network, for Image Residual Recognition (ResIST), which aims to learn latent features from unlabeled images, which is commonly used for training ResIST. The ResIST architecture was designed to be flexible to overcome the limitation of traditional ResIST architectures such as ResNet, by leveraging the deep latent representations to perform the inference task. We propose a novel architecture that learns the latent features according to its labels, based on an effective learning mechanism to improve the performance. On the other hand, it achieves the same performance without additional expensive training time. We experiment the ResIST architecture on three datasets, namely, MNIST, PASCAL VOC and ILSVRC 2017 ResIST dataset, and we obtain a novel competitive results.

An Extended Robust Principal Component Analysis for Low-Rank Matrix Estimation

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Learning Graphs from Continuous Time and Space Variables

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  • Deep Learning, A Measure of Deep Inference, and a Quantitative Algorithm

    Recurrent Residual Networks for Accurate Image Saliency DetectionIn this work we present a novel recurrent neural network architecture, Residual network, for Image Residual Recognition (ResIST), which aims to learn latent features from unlabeled images, which is commonly used for training ResIST. The ResIST architecture was designed to be flexible to overcome the limitation of traditional ResIST architectures such as ResNet, by leveraging the deep latent representations to perform the inference task. We propose a novel architecture that learns the latent features according to its labels, based on an effective learning mechanism to improve the performance. On the other hand, it achieves the same performance without additional expensive training time. We experiment the ResIST architecture on three datasets, namely, MNIST, PASCAL VOC and ILSVRC 2017 ResIST dataset, and we obtain a novel competitive results.


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