Vanilla Gradient Descent

Vanilla gradient descent, a fundamental optimization algorithm, is undergoing renewed scrutiny in various machine learning contexts, focusing on its application to complex models and its inherent limitations. Current research explores its efficacy in training non-linear models like decision trees and neural networks, investigating modifications like adaptive step sizes and sharpness-aware minimization to improve performance and generalization. These efforts aim to enhance the efficiency and robustness of vanilla gradient descent, potentially leading to more efficient training procedures and improved model accuracy across diverse applications.