Abstract: Neural network approximation of high-dimensional nonlinear functions is difficult due to the sparsity of the data in the high-dimensional data space and the need for good coverage of the ...

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Abstract: This paper considers the problem of single image super-resolution (SR). Previous example-based SR approaches mainly focus on analyzing the co-occurrence property of low resolution (LR) and ...

We propose the first local search algorithm for Euclidean clustering that attains an 𝑂(1)-approximation in almost-linear time. Specifically, for Euclidean 𝑘-Means, our algorithm achieves an ...