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.
Adam: A method for stochastic optimization
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NamedCurves+ conditions Bezier tone curves on color-naming probability maps and fuses them with a transposed-attention transformer, yielding SOTA interpretable enhancement on MIT-5K, PPR10K, MSEC and SICE.
Natural Selection (NS) dynamically reweights DNN training losses by estimating each sample's competitive status inside groups assembled as composite images.
citing papers explorer
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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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Leveraging Color Naming for Image Enhancement
NamedCurves+ conditions Bezier tone curves on color-naming probability maps and fuses them with a transposed-attention transformer, yielding SOTA interpretable enhancement on MIT-5K, PPR10K, MSEC and SICE.
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Evolution-Inspired Sample Competition for Deep Neural Network Optimization
Natural Selection (NS) dynamically reweights DNN training losses by estimating each sample's competitive status inside groups assembled as composite images.