SBL algorithms are unified under majorization-minimization with new convergence results, and a dimension-invariant neural network learns superior data-driven update rules that generalize across matrices and parameters.
Majorization-minimization algo- rithms in signal processing, communications, and machine learning
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Dual-connectivity wireless quantum networks achieve higher entanglement rates than single-connectivity by jointly optimizing user-base-station associations and rate allocations under capacity and fidelity constraints.
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Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning using Neural Networks
SBL algorithms are unified under majorization-minimization with new convergence results, and a dimension-invariant neural network learns superior data-driven update rules that generalize across matrices and parameters.
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Entanglement Rate Maximization for Dual-Connectivity Wireless Quantum Networks
Dual-connectivity wireless quantum networks achieve higher entanglement rates than single-connectivity by jointly optimizing user-base-station associations and rate allocations under capacity and fidelity constraints.