Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors.
In the offline stage, to construct the graph, user IDs and specific side information combinations of the shows are chosen to be the nodes, and click/co-click relations and view time are used to build the edges.
The unprecedented performance achieved by deep convolutional neural networks for image classification is linked primarily to their ability of capturing rich structural features at various layers within networks.
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages.