11h ago
Complex SGD and Directional Bias in Reproducing Kernel Hilbert Spaces
★★★★★
significance 2/5
The paper introduces a variant of Stochastic Gradient Descent (SGD) designed for complex-valued parameters. The authors provide convergence guarantees for this complex SGD and demonstrate its efficacy in kernel regression problems using complex reproducing kernel Hilbert spaces.
Why it matters
Optimizing complex-valued parameters may unlock more efficient convergence pathways for specialized signal processing and neural architectures.
Tags
#sgd #complex-valued neural networks #kernel regression #optimizationRelated coverage
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