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cs.LG2025
Adaptive Heavy-Tailed Stochastic Gradient Descent
Bodu Gong, Gustavo Enrique Batista, Pierre Lafaye de Micheaux
In the era of large-scale neural network models, optimization algorithms often struggle with generalization due to an overreliance on training loss. One key insight widely accepted…
cs.LG2024
AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks
Zeinab Ebrahimi, Gustavo Batista, Mohammad Deghat
Stochastic gradient descent (SGD) and its many variants are the widespread optimization algorithms for training deep neural networks. However, SGD suffers from inevitable drawbacks…