Quartz Optimizer: Robust Gradient Shaping and Bounded Adaptive Steps for Stable Deep Learning Training
Optimization plays a critical role in training deep neural networks, directly impacting convergence speed, model generalization, and stability. While existing methods such as stochastic gradient descent (SGD) and adaptive optimizers like Adam and AdamW have achieved significant success, they exhibit limitations in hand...