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Found 4 papers, 1 papers with code
Dynamic Graph Convolutional Networks Using the Tensor M-Product
In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications
Building on that work, in this paper, we use of non-negative tensor patch-based dictionaries trained on other data, such as facial image data, for the purposes of either compression or image deblurring.
A Tensor-Based Dictionary Learning Approach to Tomographic Image Reconstruction
Our approach differs from past approaches in that a) we use a third-order tensor representation for our images and b) we recast the reconstruction problem using the tensor formulation.
Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography
Specifically, we model the multi-spectral unknown as a 3-way tensor where the first two dimensions are space and the third dimension is energy.
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