@misc{shrivastava2022uglad,
author = {Shrivastava, Harsh},
title = {uGLAD: Sparse graph recovery by optimizing deep unrolled networks},
howpublished = {arXiv preprint},
year = {2022},
month = {May},
abstract = {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 multivariate Gaussian distribution, we apply a deep model on X that outputs the precision matrix Θ, which can also be interpreted as the adjacency matrix. Our model, uGLAD, builds upon and extends the state-of-the-art model GLAD to the unsupervised setting. The key benefits of our model are (1) uGLAD automatically optimizes sparsity-related regularization parameters leading to better performance than existing algorithms. (2) We introduce multi-task learning based `consensus' strategy for robust handling of missing data in an unsupervised setting. We evaluate model results on synthetic Gaussian data, non-Gaussian data generated from Gene Regulatory Networks, and present a case study in anaerobic digestion.},
url = {http://approjects.co.za/?big=en-us/research/publication/uglad-sparse-graph-recovery-by-optimizing-deep-unrolled-networks/},
}