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Found 3 papers, 2 papers with code
Gradient scarcity with Bilevel Optimization for Graph Learning
To alleviate this issue, we study several solutions: we propose to resort to latent graph learning using a Graph-to-Graph model (G2G), graph regularization to impose a prior structure on the graph, or optimizing on a larger graph than the original one with a reduced diameter.
Supervised learning of analysis-sparsity priors with automatic differentiation
In most situations, this dictionary is not known, and is to be recovered from pairs of ground-truth signals and measurements, by minimizing the reconstruction error.
Fast Graph Kernel with Optical Random Features
If this method can still be prohibitively costly for usual random features, we then incorporate optical random features that can be computed in constant time.
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