Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, toge...
Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, toge...
Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, toge...
Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, toge...
Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, toge...
The state of the practice in crash blackspot identification (BSI) has largely been driven by empirical research without much explicit attention paid to the underlying theoretical assumptions. These embedded assumptions have shaped the science of blackspot identification methodologies and developments over time. Despite the fairly extensive methodol...
Data analysis has become a critical success factor for companies in all areas. Hence, it is necessary to quickly gain knowledge from available datasets, which is becoming especially challenging in times of big data. Typical data mining tasks like cluster analysis are very time consuming even if they run in highly parallel environments like Spark cl...
Data analysis has become a critical success factor for companies in all areas. Hence, it is necessary to quickly gain knowledge from available datasets, which is becoming especially challenging in times of big data. Typical data mining tasks like cluster analysis are very time consuming even if they run in highly parallel environments like Spark cl...
Data analysis has become a critical success factor for companies in all areas. Hence, it is necessary to quickly gain knowledge from available datasets, which is becoming especially challenging in times of big data. Typical data mining tasks like cluster analysis are very time consuming even if they run in highly parallel environments like Spark cl...
Data analysis has become a critical success factor for companies in all areas. Hence, it is necessary to quickly gain knowledge from available datasets, which is becoming especially challenging in times of big data. Typical data mining tasks like cluster analysis are very time consuming even if they run in highly parallel environments like Spark cl...
