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On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning
Dictionary learning has long been studied from both optimization and probabilistic perspectives. While formulations with element-wise sparsity regularization (e.g., L1-based sparse coding) admit well-established probabilistic interpretations, many structured variants that impose global constraints l
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