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About this paper
Lei Cheng, Feng Yin, Sergios Theodoridis, Sotirios Chatzis, Tsung-Hui Chang
The article synthesizes Bayesian sparsity-aware learning across deep neural networks, Gaussian processes, and tensor decomposition. It explains how priors, evidence-based hyperparameter learning, and posterior inference can select structures while representing uncertainty, then surveys applications. On four weeks of small-cell traffic, GSMGP reports eMAPE 0.28 versus 1.12 for LSTM, a single case rather than universal superiority.
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