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uGLAD: Sparse graph recovery by optimizing deep unrolled networks
[article]
2022
arXiv
pre-print
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models are used for domain exploration and structure discovery in poorly understood domains. This work introduces a novel technique to perform sparse graph recovery by optimizing deep unrolled networks. Assuming that the input data X∈ℝ^M× D comes from an underlying
arXiv:2205.11610v2
fatcat:wb4ewzoskzeyzpqze236p7wbwi