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Found 3 papers, 2 papers with code
A Machine Learning Approach for Predicting Human Preference for Graph Layouts
In this paper, we present the first machine learning approach for predicting human preference for graph layouts.
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution
For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels.
Deep Bi-Dense Networks for Image Super-Resolution
In this way, feature information propagates from a single dense block to all subsequent blocks, instead of to a single successor.
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