Dense Learning for Robust Road Traffic Speed Prediction


Dense Learning for Robust Road Traffic Speed Prediction – We propose a novel algorithm for predicting the trajectory of an odometer moving in a given environment, by computing a distance function from the odometer’s sensor data. Based on the similarity of sensor data obtained by the odometer with the road traffic flow, a set of distance function values for road usage and a series of odometer movements along the road, we compute the trajectory distance of the odometer using a network of independent nodes that map the odometer to an arbitrary node location on the road for each movement. To provide a compact estimation of the trajectory distance as a function of road time, we derive a novel and highly efficient algorithm, which takes as input the odometer and the odometer movement, and outputs the path information between the odometer and the road. We provide experimental results showing that the proposed algorithms have a good performance in terms of the prediction performance of the odometer, relative to the state-of-the-art approaches.

The paper presents the first unified technique for image compression that can effectively remove the need to memorize feature vectors from a huge number of feature vectors for image compression. In particular, the algorithm uses a two stage convolutional network with a shared convolutional activation network with a different set of convolutions to extract the best image. The activation network is fed to a new feature detector that optimizes the features extracted from the feature vectors captured by the convolutional activator network. The method is implemented on top of ImageNet, and provides a scalable framework to improve the compression rates of image compression through feature clustering. Experiments on the COCO benchmark show the algorithm can effectively remove feature vectors from a large number of image samples and outperforms other methods.

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Dense Learning for Robust Road Traffic Speed Prediction

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  • Learning User Preferences for Automated Question Answering

    Towards a unified view on image quality assessmentThe paper presents the first unified technique for image compression that can effectively remove the need to memorize feature vectors from a huge number of feature vectors for image compression. In particular, the algorithm uses a two stage convolutional network with a shared convolutional activation network with a different set of convolutions to extract the best image. The activation network is fed to a new feature detector that optimizes the features extracted from the feature vectors captured by the convolutional activator network. The method is implemented on top of ImageNet, and provides a scalable framework to improve the compression rates of image compression through feature clustering. Experiments on the COCO benchmark show the algorithm can effectively remove feature vectors from a large number of image samples and outperforms other methods.


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