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.

We present the concept of local , and derive a new algorithm for the classification of deep neural networks. The proposed algorithm is not global but is used to model a specific distribution of input images. Based on the local distribution, a deep CNN is trained to learn an image representation over the given distribution. This architecture leads to a new class of deep networks with two main advantages. First, they are more memory efficient (using only 1% energy, compared with the previous layer of deep CNNs). Second, the architectures are scalable (using only 60,000 pixels per network). We use this new architecture to tackle the problem of image classification, and report an impressive success rate of 96.77% on the MNIST dataset. Our model can be trained and used to classify images with 100×100 times speed on standard benchmarks.

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Learning from Continuous Events with the Gated Recurrent Neural Network

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  • Parsimonious regression maps for time series and pairwise correlations

    DNN-CMA: A Deep Recurrent Model for Image Classification and SegmentationWe present the concept of local , and derive a new algorithm for the classification of deep neural networks. The proposed algorithm is not global but is used to model a specific distribution of input images. Based on the local distribution, a deep CNN is trained to learn an image representation over the given distribution. This architecture leads to a new class of deep networks with two main advantages. First, they are more memory efficient (using only 1% energy, compared with the previous layer of deep CNNs). Second, the architectures are scalable (using only 60,000 pixels per network). We use this new architecture to tackle the problem of image classification, and report an impressive success rate of 96.77% on the MNIST dataset. Our model can be trained and used to classify images with 100×100 times speed on standard benchmarks.


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