CT-AGD accelerates first-order optimization in deep learning by using finite-difference curvature estimates and noise-mitigation heuristics, achieving equivalent accuracy with 33% fewer training epochs and overhead comparable to Adam.
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Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
CT-AGD accelerates first-order optimization in deep learning by using finite-difference curvature estimates and noise-mitigation heuristics, achieving equivalent accuracy with 33% fewer training epochs and overhead comparable to Adam.