Predicting Daily Activity with a Deep Neural Network


Predicting Daily Activity with a Deep Neural Network – We present the first real-time and scalable approach for the problem of predicting the outcome of a simulated election. The prediction is performed during the course of a daily election. One particular feature of this challenge is that even for a few days, the forecast accuracy of the prediction is always significantly lower than that of the observed forecast.

We consider the problem of extracting salient and unlabeled visual features from a text, and thus derive an approach that works well for image classification. As the task is multi-dimensional, we design a deep Convolutional Neural Network (CNN) that learns a visual feature descriptor by minimizing an image’s weighting process using the sum of the global feature maps, then learning a descriptor from the weights in the final feature map. We illustrate the effectiveness of CNNs trained on the Penn Treebank (TT) dataset and a standard benchmark task for image classification.

In the context of deep learning, deep neural networks (DNN’s) have recently gained popularity due to their impressive performance on most tasks, such as object classification, language understanding and object recognition. However, for the most part, DNN’s are not fully-connected. In order to handle multiple layers, these layers are used to perform the classification of the input image, which has been a challenging task for deep neural networks. In this work, we propose a novel hierarchical LSTM architecture, which is capable of being stacked to provide a higher level learning capability. Unlike the previous hierarchical architectures, the learned LSTM structures are connected to the learned models by a novel set of hidden layers, which can be easily updated via a back-propagation algorithm. Moreover, we show that the learnt LSTM can directly be used for segmentation, which is a highly desirable task for neural networks in this context. Experimental analysis using a simulated human benchmark dataset demonstrates that the proposed architecture is significantly better for the proposed task.

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Predicting Daily Activity with a Deep Neural Network

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  • Hierarchical Learning for Distributed Multilabel Learning

    Dense-2-Type CNN for Stereo Visual OdometryIn the context of deep learning, deep neural networks (DNN’s) have recently gained popularity due to their impressive performance on most tasks, such as object classification, language understanding and object recognition. However, for the most part, DNN’s are not fully-connected. In order to handle multiple layers, these layers are used to perform the classification of the input image, which has been a challenging task for deep neural networks. In this work, we propose a novel hierarchical LSTM architecture, which is capable of being stacked to provide a higher level learning capability. Unlike the previous hierarchical architectures, the learned LSTM structures are connected to the learned models by a novel set of hidden layers, which can be easily updated via a back-propagation algorithm. Moreover, we show that the learnt LSTM can directly be used for segmentation, which is a highly desirable task for neural networks in this context. Experimental analysis using a simulated human benchmark dataset demonstrates that the proposed architecture is significantly better for the proposed task.


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