This research introduces a hybrid approach to accelerate convergence in second-order optimization by utilizing an online finite difference approximation of the diagonal Hessian matrix and fuzzy inferencing of hyperparameters. The content covers deep learning models, second-order optimization methods, various techniques like CG, Newton's method, quasi-Newton methods, stochastic quasi-Newton methods, HF method, sub-sampled HF method, and more. It delves into the diagonal Hessian approximation technique, finite differences, and methodologies proposed by researchers. Additionally, it discusses Fuzzy Logic Based Scheduling with a literature review on Fuzzy Expert Systems and their applications in learning rate optimization. The content also presents a new method for diagonal Hessian approximation in deep learning optimization. Experimental results on ImageNet dataset comparing SALO against Adam, AdamW, and SGD are discussed along with empirical performance analysis.
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by Abdelaziz Be... at arxiv.org 03-26-2024
https://arxiv.org/pdf/2403.15416.pdfDeeper Inquiries