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Optimal Hyperparameter $ε$ for Adaptive Stochastic Optimizers through Gradient Histograms
In this manuscript, we introduce a new framework based on gradient histograms to analyze and justify important attributes of adaptive optimizers, such as their optimal performance and the relationships and dependencies among hyperparameters.
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems
Convolutional sparse representation (CSR), shift-invariant model for inverse problems, has gained much attention in the fields of signal/image processing, machine learning and computer vision.
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