Learning from Continuous Events with the Gated Recurrent Neural Network


Learning from Continuous Events with the Gated Recurrent Neural Network – We present a novel deep-learning technique to automatically learn the spatial location of objects in a scene, which is based on Recurrent Neural Networks (RNN) and can achieve high accuracies by learning the object location from a large set of object instances. In this work, we provide state-of-the-art classification accuracies at an accuracy of 10.81%. Our method can be embedded into many different RNN architectures and can be applied to datasets. We demonstrate the effectiveness of our approach in a supervised task where we use Gated Recurrent Neural Network (GRNN) to extract object-oriented objects and then apply the method at the scene.

A new class of feature learning methods based on deep generative models based on latent variables is emerging. The approach, inspired by the deep generative model (GMM) approach, is a fully convolutional, neural network architecture which simultaneously learns multiple features. The first feature is learnt from the output of deep GMM. The second feature is used to detect the relationships between labels and labels have been extracted. These labels are learnt through a hierarchical structure. To learn these hierarchical structures, a novel deep neural network was trained to predict the feature structure. The supervised feature learning was performed by using supervised regression classifiers. The results of the classifiers show that the supervised network outperforms the fully convolutional GMM-based classifiers on a small number of classification tasks. Also, the proposed network outperforms both supervised and GMM-based feature learning methods.

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Learning from Continuous Events with the Gated Recurrent Neural Network

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  • Towards a Theory of a Semantic Portal

    Interpretable Feature Learning: A SurveyA new class of feature learning methods based on deep generative models based on latent variables is emerging. The approach, inspired by the deep generative model (GMM) approach, is a fully convolutional, neural network architecture which simultaneously learns multiple features. The first feature is learnt from the output of deep GMM. The second feature is used to detect the relationships between labels and labels have been extracted. These labels are learnt through a hierarchical structure. To learn these hierarchical structures, a novel deep neural network was trained to predict the feature structure. The supervised feature learning was performed by using supervised regression classifiers. The results of the classifiers show that the supervised network outperforms the fully convolutional GMM-based classifiers on a small number of classification tasks. Also, the proposed network outperforms both supervised and GMM-based feature learning methods.


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