03 Feb 2023
Research clarifies the computational efficiency and implicit bias of Gradient Regularization (GR) in deep learning. The study introduces efficient finite-difference methods for GR computation, demonstrating their improved generalization and revealing theoretical connections to flat minima and other optimization techniques like flooding and SAM.
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